METHOD AND APPARATUS OF ENCODING/DECODING POINT CLOUD GEOMETRY DATA SENSED BY AT LEAST ONE SENSOR

Abstract

A method and apparatus is capable of encoding and decoding a point cloud sensed by any type of sensor following a sensing path. The method obtains coarse representations of sensed points and encodes controls points of a two-dimensional curve representing the sensing path and the coarse representations. The control points are decoded, the two-dimensional curve representing the sensing path is obtained by interpolation between the control points and coarse representations of points are decoded. Points of the point cloud are then reconstructed from the decoded two-dimensional curve representing the sensing path and the decoded coarse representations.

Description

FIELD

The present application generally relates to point cloud compression and, in particular to methods and apparatus of encoding/decoding point cloud geometry data sensed by at least one sensor.

BACKGROUND

The present section is intended to introduce the reader to various aspects of art, which may be related to various aspects of at least one embodiments of the present application that is described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present application.

As a format for the representation of 3D data, point clouds have recently gained traction as they are versatile in their capability in representing all types of physical objects or scenes.

SUMMARY

The following section presents a simplified summary of at least one embodiment in order to provide a basic understanding of some aspects of the present application. This summary is not an extensive overview of an embodiment. It is not intended to identify key or critical elements of an embodiment. The following summary merely presents some aspects of at least one of the embodiments in a simplified form as a prelude to the more detailed description provided elsewhere in the document.

According to a first aspect of the present application, there is provided a method of encoding points of a point cloud into a bitstream of encoded point cloud data, wherein the points of the point cloud are sensed by at least one sensor according to a sensing path represented by a two-dimensional curve in a two-dimensional angular coordinate space comprising an azimuthal coordinate representative of an azimuthal angle representing a sensing angle of a sensor relative to a referential and an elevation coordinate representative of an elevation angle of a sensor relative to a horizontal referential plane; and wherein the method comprises: encoding, into the bitstream, control points used to obtain the two-dimensional curve by interpolation between said control points; and encoding, into the bitstream, the points of the point cloud relatively to coarse points coarsely representing angular coordinates of the points of the point cloud, a coarse point being defined in the two-dimensional angular coordinate space from a sample index relative to a sensing time instant along the two-dimensional curve and one sensor index associated with a sensor.

According to a second aspect of the present application, there is provided a method of decoding points of a point cloud from a bitstream of encoded point cloud data, wherein the method comprises: decoding control points from the bitstream, the control points being used to obtain a two-dimensional curve representing a sensing path in a two-dimensional curve in a two-dimensional angular coordinate space comprising an azimuthal coordinate representative of an azimuthal angle representing a sensing angle of a sensor relative to a referential and an elevation coordinate representative of an elevation angle of a sensor relative to a horizontal referential plane; decoding, from the bitstream, the points of the point cloud relatively to coarse points having angular coordinates in the two-dimensional angular coordinate space that coarsely represent angular coordinates of the points of the point cloud, a coarse point being defined in the two-dimensional angular coordinate space from a sample index relative to a sensing time instant along the two-dimensional curve and one sensor index associated with a sensor.

According to a third aspect of the present application, there is provided an apparatus of encoding points of a point cloud sensed by at least one sensor associated with a sensor index into a bitstream of encoded point cloud data. The apparatus comprises one or more processors configured to carry out a method according to the first aspect of the present application.

According to a fourth aspect of the present application, there is provided an apparatus of decoding points of a point cloud sensed by at least one sensor associated with a sensor index from a bitstream of encoded point cloud data. The apparatus comprises one or more processors configured to carry out a method according to the second aspect of the present application.

According to a fifth aspect of the present application, there is provided a non-transitory storage medium carrying instructions of program code for executing a method according to the first aspect of the present application.

According to a sixth aspect of the present application, there is provided a non-transitory storage medium carrying instructions of program code for executing a method according to the second aspect of the present application.

The specific nature of at least one of the embodiments as well as other objects, advantages, features and uses of said at least one of embodiments will become evident from the following description of examples taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made, by way of example, to the accompanying drawings which show embodiments of the present application, and in which:

FIG. 1 shows a side view of a sensor head and some of its parameters in accordance with prior art;

FIG. 2 shows a top view of the sensor head and some of its parameters in accordance with prior art;

FIG. 3 shows a regular distribution of data sensed by a spinning sensor head in accordance with prior art;

FIG. 4 shows a representation of a point of a point cloud in a 3D space in accordance with prior art;

FIG. 5 shows schematically an example of a sensor head able to sense a real scene following a programmable sensing path in accordance with prior art;

FIG. 6 shows schematically an example of a sensor head able to sense a real scene following a programmable sensing path according to different sensing frequencies in accordance with prior art;

FIG. 7 shows schematically an example of a sensor head able to sense a real scene following a programmable zigzag sensing path according to different sensing frequencies in accordance with prior art;

FIG. 8 shows schematically a single sensor head able to sense a real scene following a programmable zigzag sensing path according to different sensing frequencies;

FIG. 9 shows a schematic block diagram of steps of a method 100 of encoding a point cloud into a bitstream of encoded point cloud data in accordance with at least one embodiment;

FIG. 10 illustrates schematically a representation of a sensing path SP in accordance with some embodiments;

FIG. 11 illustrates schematically a sensing path SP sampled by a sensing scheme in accordance with some embodiments;

FIG. 12 illustrates schematically a sensing path SP sampled by a sensing scheme in accordance with some embodiments;

FIG. 13 illustrates schematically a sensing path SP sampled by a sensing scheme in accordance with some embodiments;

FIG. 14 illustrates schematically a sensing path SP sampled by a sensing scheme in accordance with some embodiments;

FIG. 15 illustrates schematically a sensing path SP sampled by a sensing scheme in accordance with some embodiments;

FIG. 16 illustrates schematically an example of an ordering of coarse points in accordance with some embodiments;

FIG. 17 illustrates schematically a representation of ordered points in a two-dimensional coordinate (s,λ) space in accordance with some embodiments;

FIG. 18 illustrates schematically calculating of order index differences in accordance with some embodiments;

FIG. 19 illustrates schematically obtaining of a coarse point from a sensing point in accordance with some embodiments;

FIG. 20 shows a schematic block diagram of steps of a method 200 of decoding a point cloud from a bitstream of encoded point cloud data in accordance with at least one embodiment; and

FIG. 21 illustrates a schematic block diagram of an example of a system in which various aspects and embodiments are implemented.

Similar reference numerals may have been used in different figures to denote similar components.

DETAILED DESCRIPTION

At least one of the embodiments is described more fully hereinafter with reference to the accompanying figures, in which examples of at least one of the embodiments are illustrated. An embodiment may, however, be embodied in many alternate forms and should not be construed as limited to the examples set forth herein. Accordingly, it should be understood that there is no intent to limit embodiments to the particular forms disclosed. On the contrary, the disclosure is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present application.

At least one of the aspects generally relates to point cloud encoding and decoding, one other aspect generally relates to transmitting a bitstream generated or encoded and one other aspects relates to receiving/accessing a decoded bitstream.

Point clouds may be used for various purposes such as culture heritage/buildings in which objects like statues or buildings are scanned in 3D in order to share the spatial configuration of the object without sending or visiting it. Also, it is a way to ensure preserving the knowledge of the object in case it may be destroyed; for instance, a temple by an earthquake. Such point clouds are typically static, colored and huge.

Another use case is in topography and cartography in which using 3D representations allows for maps that are not limited to the plane and may include the relief. Google Maps is now a good example of 3D maps but uses meshes instead of point clouds. Nevertheless, point clouds may be a suitable data format for 3D maps and such point clouds are typically static, colored and huge.

Virtual Reality (VR), Augmented Reality (AR) and immersive worlds have recently become a hot topic and are foreseen by many as the future of 2D flat video. The basic idea is to immerse the viewer in a surrounding environment, in contrast to a standard TV that only allows the viewer to look at the virtual world in front of him/her. There are several gradations in the immersivity depending on the freedom of the viewer in the environment. A point cloud is a good format candidate for distributing VR/AR worlds.

The automotive industry, and more particularly foreseen autonomous cars, are also domains in which point clouds may be intensively used. Autonomous cars should be able to “probe” their environment to make good driving decisions based on the detected presence and nature of their immediate nearby objects and road configuration.

A point cloud is a set of points located in a tridimensional (3D) space, optionally with additional values attached to each of the points. These additional values are usually called attributes. Attributes may be, for example, three-component colors, material properties like reflectance and/or two-component normal vectors to a surface associated with a point.

A point cloud is thus a combination of geometry data (locations of the points in a 3D space usually represented by 3D cartesian coordinates x,y and z) and attributes.

Point clouds may be sensed by various types of devices like an array of cameras, depth sensors, lasers (light detection and ranging, also known as Lidars), radars, or may be computer-generated (for example in movie post-production). Depending on the use cases, point clouds may have from thousands to up to billions of points for cartography applications. Raw representations of point clouds require a very high number of bits per point, with at least a dozen of bits per cartesian coordinate x, y or z, and optionally more bits for the attribute(s), for instance three times 10 bits for the colors.

It is important in many applications to be able to either distribute point clouds to an end-user or store them in a server by consuming only a reasonable amount of bitrate or storage space, while maintaining an acceptable (or preferably very good) quality of experience. Efficient compression of these point clouds is a key point in order to make the distribution chain of many immersive worlds practical.

Compression may be lossy (like in video compression) for the distribution to and visualization by an end-user, for example on AR/VR glasses or any other 3D-capable device. Other use cases do require lossless compression, like medical applications or autonomous driving, to avoid altering the results of a decision obtained from the subsequent analysis of the compressed and transmitted point cloud.

Until recently, point cloud compression (aka PCC) was not addressed by the mass market and no standardized point cloud codec was available. In 2017, the standardization working group ISO/JCT1/SC29/WG11, aka Moving Picture Experts Group or MPEG, has initiated work items on point cloud compression. This has led to two standards, namely

• • MPEG-I part 5 (ISO/IEC 23090-5) or Video-based Point Cloud Compression (aka V-PCC)
  • MPEG-I part 9 (ISO/IEC 23090-9) or Geometry-based Point Cloud Compression (aka G-PCC)

The V-PCC coding method compresses a point cloud by performing multiple projections of a 3D object to obtain 2D patches that are packed into an image (or a video when dealing with dynamic point clouds). Obtained images or videos are then compressed using already existing image/video codecs, allowing for the leverage of already deployed image and video solutions. By its very nature, V-PCC is efficient only on dense and continuous point clouds because image/video codecs are unable to compress non-smooth patches as would be obtained from the projection of, for example, Lidar-sensed sparse geometry data.

The G-PCC coding method has two schemes for the compression of a sensed sparse geometry data.

The first scheme is based on an occupancy tree, being locally any type of tree among octree, quadtree or binary tree, representing the point cloud geometry. Occupied nodes are split down until a certain size is reached, and occupied leaf nodes provide the 3D locations of points, typically at the center of these nodes. The occupancy information is carried by occupancy flags signaling the occupancy status of each of the child nodes of nodes. By using neighbor-based prediction techniques, high level of compression of the occupancy flags can be obtained for dense point clouds. Sparse point clouds are also addressed by directly coding the position of point within a node with non-minimal size, by stopping the tree construction when only isolated points are present in a node; this technique is known as Direct Coding Mode (DCM).

The second scheme is based on a predictive tree in which each node represents the 3D location of one point and the parent/child relation between nodes represents spatial prediction from parent to children. This method can only address sparse point clouds and offers the advantage of lower latency and simpler decoding than the occupancy tree. However, compression performance is only marginally better, and the encoding is complex, relatively to the first occupancy-based method, because the encoder must intensively look for the best predictor (among a long list of potential predictors) when constructing the predictive tree.

In both schemes, attribute (de) coding is performed after complete geometry (de) coding, leading practically to a two-pass coding. Thus, the joint geometry/attribute low latency is obtained by using slices that decompose the 3D space into sub-volumes that are coded independently, without prediction between the sub-volumes. This may heavily impact the compression performance when many slices are used.

Combining together requirements on encoder and decoder simplicity, on low latency and on compression performance is still a problem that has not been satisfactory solved by existing point cloud codecs.

An important use case is the transmission of sparse geometry data sensed by at least one sensor mounted on a moving vehicle. This usually requires a simple and low latency embarked encoder. Simplicity is required because the encoder is likely to be deployed on computing units which perform other processing in parallel, such as (semi-) autonomous driving, thus limiting the processing power available to the point cloud encoder. Low latency is also required to allow for fast transmission from the car to a cloud in order to have a real-time view of the local traffic, based on multiple-vehicle acquisition, and take adequate fast decision based on the traffic information. While transmission latency can be low enough by using 5G, the encoder itself shall not introduce too much latency due to coding. Also, compression performance is extremely important since the flow of data from millions of cars to the cloud is expected to be extremely heavy.

Specific priors related to spinning Lidar-sensed sparse geometry data have been already exploited in G-PCC and have led to very significant gains of compression.

First, G-PCC exploits the elevation angle (relative to the horizontal ground) of sensing from a spinning Lidar head 10 as depicted on FIGS. 1 and 2. A Lidar head 10 comprises a set of sensors 11 (for example lasers), here five sensors are represented. The spinning Lidar head 10 may spin around a vertical axis z to sense geometry data of a physical object. Lidar-sensed geometry data is then represented in spherical coordinates (r_3D, ϕ, θ), where r_3D is the distance of a point P from the Lidar head's center, ϕ is an azimuthal angle of the Lidar head's spin relative to a referential, and θ is an elevation angle of a sensor k of the spinning Lidar head 10 relative to a horizontal referential plane.

A regular distribution along the azimuthal angle has been observed on Lidar-sensed data as depicted on FIG. 3. This regularity is used in G-PCC to obtain a quasi 1D representation of the point cloud where, up to noise, only a radius r_3D belongs to a continuous range of values while the angles ϕ and θ take only a discrete number of values ϕ_i ∀i=0 to I−1 where I is a number of azimuthal angles used for the sensing of the points and θ_j ∀j=0 to J−1 where J is a number of sensors of the spinning Lidar head 10. Basically, G-PCC represents Lidar-sensed sparse geometry data on a two-dimensional (discrete) angular coordinate space (ϕ, θ) as depicted on FIG. 3, together with a radius value r_3D for each point.

This quasi 1D property has been exploited in G-PCC in both the occupancy tree and the predictive tree by predicting, in the spherical coordinate space, the location of a current point based on an already coded point by using the discrete nature of angles.

More precisely, the occupancy tree uses DCM intensively and entropy codes the direct locations of points within a node by using a context-adaptive entropy coder. Contexts are then obtained from the local conversion of the point locations into angular coordinates (ϕ, θ) and from the location of these angular coordinates relative to discrete angular coordinates (ϕ_i, θ_j) obtained from precedingly coded points. The predictive tree directly codes a first version of a point location in the angular coordinates (r_2D, ϕ, θ), where r_2D is the projected radius on the horizontal xy plane as depicted on FIG. 4, using the quasi 1D nature (r_2D, ϕ_i, θ_j) of this angular coordinate space. Then, spherical coordinates (r_2D, ϕ, θ) are converted into 3D cartesian coordinates (x,y,z) and a xyz residual is coded to tackle the errors of coordinate conversion, the approximation of elevation and azimuthal angles and potential noise.

G-PCC does use the angular priors to better compress spinning Lidar-sensed sparse geometry data but does not adapt the coding structure to the order of sensing. By its very nature, the occupancy tree must be coded down to its last depth before outputting a point. This occupancy is coded in the so-called breadth-first order: the occupancy of the root node is first coded, indicating its occupied child nodes; then the occupancy for each of the occupied child nodes is coded, indicating the occupied grand-child nodes; and so on iteratively over the tree depth until leaf nodes can be determined and the corresponding points are provided/output to an application or to the attribute(s) coding scheme. Regarding the predictive tree, the encoder is free to choose the order of point in the tree, but to obtain good compression performance, to optimize the prediction accuracy, G-PCC proposes to code one tree per sensor. This has mainly the same drawback as using one coding slice per sensor, i.e. non-optimal compression performance because prediction between sensors is not allowed and does not provide encoder low latency. Worse, one should have one coding processing per sensor and the number of core coding units should equal the number of sensors; this is not practical.

In brief, in a framework of a spinning sensors head used for sensing sparse geometry data of a point cloud, prior arts do not solve the problem of combining encoding and decoding simplicity, low latency and compression performance.

Moreover, sensing sparse geometry data of point cloud by using spinning sensors head has some drawbacks and other types of sensor head may be used.

The mechanical parts generating the spin (rotation) of a spinning sensors head are prone to breakage and are costly. Also, by construction, the angle of view is necessarily 2π. This does not allow to sense a particular region of interest with high frequency, for instance it may be more interesting to sense in front of a vehicle than behind. Practically in most cases, when a sensor is attached to a vehicle, most of the 2π angle of view is screened by the vehicle itself and the screened angle of view does not need to be sensed.

New types of sensors have emerged recently, allowing for a more flexible selection of the region to be sensed. In most recent designs, a sensor can be more freely and electronically (thus avoiding fragile mechanical parts) moved to obtain a large variety of sensing path in the 3D scene as depicted on FIG. 5. On this FIG. 5, a set of four sensors is shown. Their relative sensing directions, i.e. azimuthal and elevation angles, are fixed relative to each other, however they overall sense a scene following a programmable sensing path depicted by dashed lines on the two-dimensional angular coordinate (ϕ,θ) space. Points of the point cloud may then be sensed regularly along the sensing path. Some sensor head may also adapt their sensing frequency by increasing their sensing frequency when a region of interest R has been detected as illustrated on FIG. 6. Such a region of interest R may be relative to a close object, a moving object, or any object (pedestrian, other vehicle, etc.) precedingly segmented, for instance in a previous frame, or dynamically segmented during the sensing. FIG. 7 illustrates another example of a sensing path (typical zigzag sensing path) used by a sensor head comprising two sensors able to increase their sensing frequencies when a region of interest has been detected (grey shaded points and grey hashed points). Using a zigzag sensing path may be advantageously used to sense a limited (azimuthal) angular sector of a 3D scene. Because sensors may be attached to a vehicle, their viewports of interest are necessarily limited by the presence of the vehicle itself that obstructs the scene, unless the sensors are located at the top of the car. Consequently, sensors with a limited probing angular sector are of high interest and easier to integrate to a vehicle.

As is illustrated on FIG. 8, a sensor head comprising a single sensor may also be used to sense multiple positions (two vertical positions on FIG. 8), for instance using reflections on mirrors oscillating with rotations (here vertical rotations). In that case, instead of using a set of sensors, a single sensor at different angular positions (i.e. with different elevation angle on FIG. 8) along a sensing path (here a zigzag sensing path) is used mimicking a sensing using a set of multiple sensors. For the sake of simplicity, in the following descriptions and claims, the “sensor head” may refer to a set of physical sensors or a set of sensing elevation indexes mimicking a set of sensors as well. Also, one skilled in the art will understand when “a sensor” could also refer to a sensor in each sensing elevation index position.

Combining together requirements on encoder and decoder simplicity, on low latency and on compression performance of point cloud sensed by any type of sensors is still a problem that has not been satisfactory solved by existing point cloud codecs.

At least one embodiment of the present application has been devised with the foregoing in mind.

Moreover, the present aspects are not limited to MPEG standards such as MPEG-I part 5 or part 9 that relate to the Point Cloud Compression, and may be applied, for example, to other standards and recommendations, whether pre-existing or future-developed, and extensions of any such standards and recommendations (including MPEG-I part 5 and part 9). Unless indicated otherwise, or technically precluded, the aspects described in the present application may be used individually or in combination.

In brief, the present invention relates to encoding/decoding a point cloud sensed by any type of sensor head according to a sensing path. Some examples of such sensor head and sensing paths are shown on FIGS. 3, 5-8 for only illustrative purpose because any type of sensor(s) may be used to sense points of a point cloud following any sensing path. The sensing path is represented by a two-dimensional curve in a two-dimensional coordinate space comprising an azimuthal coordinate representative of an azimuthal angle representing a sensing angle of a sensor relative to a referential and an elevation coordinate representative of an elevation angle of a sensor relative to a horizontal referential plane. The method comprises encoding, into the bitstream, control points used to obtain the two-dimensional curve by interpolation between said control points; and encoding, into the bitstream, the points of the point cloud relatively to coarse points coarsely representing angular coordinates of the points of the point cloud; a coarse point is defined in the two-dimensional angular coordinate space from a sample index relative to a sensing time instant along the two-dimensional curve and one sensor index associated with a sensor.

The present invention encodes control points to reconstruct a two-dimensional curve representing a sensing path at the decoding side. Encoding control points requires a very few extra bandwidth for encoding a sensing path.

FIG. 9 illustrates a schematic block diagram of steps of a method 100 of encoding a point cloud into a bitstream of encoded point cloud data in accordance with at least one embodiment.

Geometry data of the point cloud, i.e. the 3D locations of points of the point cloud, is sensed by any sensor head including a spinning sensors head and/or a single sensor head mimicking multiple sensors.

In step 110, a sensing path SP is represented as a two-dimensional curve defined in a two-dimensional angular coordinate (ϕ, θ) space. The sensing path SP is obtained from sensing characteristics. The two-dimensional angular coordinate (ϕ, θ) space comprises an azimuthal coordinate ϕ representative of an azimuthal angle representing a sensing angle of a sensor relative to a referential and an elevation coordinate θ representative of an elevation angle of a sensor relative to a horizontal referential plane.

In step 110, as illustrated on FIG. 10, the two-dimensional curve (sensing path) SP may be represented by at least one control point ω_k having angular coordinates (ϕ_k, θ_k) in the two-dimensional angular coordinate (ϕ, θ) space. The two-dimensional curve SP may then be obtained by interpolation between the control points ok. For example, a linear interpolation may be used. In another other example, higher order interpolation may be used like third order splines. Any other well-known interpolation function may be used.

Control points ω_k do not need to correspond to some sensing time instant. However, it may be advantageous to have the first control point ω₀ being equal to the first sensing time instant as it avoids associating explicitly a sensing time instant to the first control point ω₀ and thus avoids coding this sensing time instant.

In some embodiments of step 110, one sensing characteristics imposes that the two-dimensional curve SP is closed by an extra interpolation between the last point and the first point of the two-dimensional curve SP.

In some embodiments of step 110, one other sensing characteristics imposes that the two-dimensional curve SP is a fixed path over which the sensing may loop continuously.

In some embodiments of step 110, one other sensing characteristics imposes that the two-dimensional curve SP is a zigzag path covering at least a part of the viewport of a sensor.

The sensing path SP is used to sense the points P of the point cloud according to ordered coarse points P_λ(s). A coarse point P_λ(s) represents a potential location in the two-dimensional coordinate (ϕ, θ) space of a sensed point P of the point cloud. Each coarse point P_λ(s) is defined in the two-dimensional angular coordinate (ϕ, θ) space from one sample index s relative to a sensing time instant along the two-dimensional curve SP, and one sensor index λ associated with a sensor. A coarse point P_λ(s) corresponds to a coarse representation of a potential sensed point of the cloud.

Each sample index s corresponds to a sensing point P_over(s) in the two-dimensional angular coordinate (ϕ, θ) space. A sensing point P_over(s) is representative of a referential for determining angular coordinates of points sensed by the sensor head.

In one example, a sensing point P_over(s) is representative of a mean direction pointed by the sensor head when sensing one s-th sample of points. The angular coordinates (ϕ_over, θ_over), as a function of the sample index s, of a sensing point P_over(s) on the two-dimensional curve SP are obtained from a given sensing scheme.

In one embodiment of step 110, illustrated on FIG. 11, the sensing scheme provides a uniform sampling of the two-dimensional curve SP. Sensing time instants (sample indices s corresponding to sensing points P_over(s)) are then regularly placed along the two-dimensional curve SP. In this case, the distance, measured along the two-dimensional curve SP, between two consecutive sensing points P_over(s) and P_over(s+1) is constant.

In one embodiment of step 110, the sensing scheme may provide a repartition of the sensing time instants (sensing point P_over(s)) along the two-dimensional curve SP to get a uniform sampling of the azimuthal coordinates ϕ of said sensing point P_over(s). In this case, the azimuthal distance between the azimuthal angles ϕ(P_over(s)) and ϕ(P_over(s+1)) associated with two consecutive sensing points P_over(s) and P_over(s+1) is constant.

In one embodiment of step 110, the sensing scheme may provide a repartition of the sensing time instants (sensing point P_over(s)) along the two-dimensional curve SP to get a uniform sampling of the elevation coordinates θ of said sensing point P_over(s). In this case, the elevation distance between the elevation angles θ(P_over(s)) and θ(P_over(s+1)) associated with two consecutive sensing points P_over(s) and P_over(s+1) is constant.

In one embodiment of step 110, illustrated on FIG. 12, the sensing scheme may define a two-dimensional curve SP comprising a same number of sensing time instants (sensing points P_over(s)) between each pair of control points ω_k−1 and ω_k. Control points ω_k−1 and ω_k do correspond to some sensing points. On FIG. 12, it is assumed that there are two sensing time instants (sensing points P_over(s)) between the control points ω_k−1 and ω_k.

In one variant, the sensing time instants may be uniformly spaced between each pair of control points ω_k−1 and ω_k.

Any other spatial repartition of the sensing time instants along the two-dimensional curve SP may be used.

In one variant, the number N_k of sensing time instants between each pair of control points ω_k−1 and ω_k may be encoded in the bitstream B.

In one variant, the number N_k of sensing time instants between each pair of control points ω_k−1 and ω_k may be encoded relatively to at least one another number of sensing time instants.

This variant is advantageous because it allows the number N_k of sensing time instants to be adapted according to regions of interest along the sensing path: a more important number of sensing time instants being allowed in regions of interest.

As a non-restrictive example, the number N_k of sensing time instants between a pair of control points ω_k−1 and ω_k may be encoded relatively to the last encoded number N_k−1 of sensing time instants between the pair of control points ω_k−2 and ω_k−1. The number N_k of sensing time instants may also be encoded relatively to a combination of multiples previously encoded numbers of sensing time instants.

In one variant, the number N_k of sensing time instants or a residual derived from the number N_k and at least one previously encoded number of sensing time instants may be compressed using an entropy coder like CABAC after binarization of their values.

Any other entropy coder may be used.

In some embodiments of step 110, illustrated on FIG. 13, the sensing scheme may associate a sampling index s_k with each control point ω_k.

The sampling index s_k is related to the s_k-th sensing time instant (sensing points P_over(s_k)).

This embodiment of step 110 allows to obtain a two-dimensional curve SP which is parametrized by the sampling index s.

In one variant, the sampling indices s_k may be encoded in the bitstream B together with the angular coordinates (ϕ_k, θ_k) of the control points ω_k.

This variant is advantageous because it allows flexibility in choosing the control points ω_k independently on the density of sensed points, thus reducing the number of control points ω_k where the two-dimensional curve SP is easily interpolated.

In one variant, a sampling index s_k may be encoded relatively to at least one another sampling index associated with at least one other control point.

As a non-restrictive example, the sampling index s_k may be encoded relatively to the last encoded sampling index s_k−1. The sampling index s_k may also be encoded relatively to a combination of multiple previously encoded sampling indices associated with control points.

In one variant, the sampling index s_k or a residual derived from the sampling index value s_k and at least one previously encoded sampling index may be compressed using an entropy coder like CABAC after binarization of their values.

Any other entropy coder may be used.

Consequently, following such a sensing scheme, there are N_k=s_k−s_k−1−1 sensing time instants (sensing points) between the two control points ω_k−1 and ω_k.

In one variant, the sensing time instants are uniformly spaced between each pair of control points ω_k−1 and ω_k as depicted on FIG. 13.

Any other spatial repartition of the sensing time instants along the two-dimensional curve SP may be used.

FIG. 14 illustrates how the two-dimensional curve SP of a spinning sensor head is represented using a sensing scheme with parameterization by sampling index s_k. Only two control points ω₀ and ω₁ are needed to define the two-dimensional curve SP with same elevation angle coordinate θ. They have respective azimuthal coordinates 0 and 2π. Assuming the spinning sensor head senses N_turn sensing time instants per 2π turn, the respective sampling indexes for the two control points are s₀=1 and s₁=N_turn. By doing so, there are N_turn sensing time instants, for s=1, . . . , N_turn, that are uniformly distributed over azimuthal angles from 0 to 2π.

In some embodiments of step 110, illustrated on FIG. 15, the sensing scheme may associate a timestamp parameter t_k with each control point ω_k.

The timestamp parameter t_k represents the sensing time instant when the sensor head points to a control point ω_k.

This embodiment of step 110 allows to obtain a two-dimensional curve SP which is parametrized by sensing time.

In one variant, the timestamp parameter t_k may be encoded in the bitstream B together with the angular coordinates (ω_k, θ_k) of the control points ω_k.

This variant is advantageous because it allows time intervals between timestamp parameter values to be adapted according to regions of interest along the two-dimensional curve: short time intervals are allowed in regions of interest.

In one variant, a timestamp parameter value t_k may be encoded relatively to at least one another timestamp parameter value.

As a non-restrictive example, the timestamp parameter value t_k may be encoded relatively to the last encoded timestamp parameter t_k−1. The timestamp parameter value t_k may also be encoded relatively to a combination of multiple previously encoded timestamp parameter values.

In one variant, the timestamp parameter value t_k or a residual derived from the timestamp parameter value t_k and at least one previously encoded timestamp parameter value may be compressed using an entropy coder like CABAC after binarization of their values.

Any other entropy coder may be used.

In one variant, obtaining a sensing point P_over(s) may comprise obtaining a sensing time t_s and next obtaining the sensing point P_over (t_s)=P_over(s) by straightforward temporal interpolation between control points ω_k as illustrated on FIG. 15.

In one variant, a sensing time instant t_s may be obtained using a temporal step Δt such that t_s+1=t_s+Δt.

In one variant the temporal step Δt is a fixed value leading to a uniform repartition of the sensing time instants along the two-dimensional curve SP.

In one variant, the temporal step Δt is known beforehand at the decoder and only the first sensing time instant t₀ is encoded into the bitstream B.

In one other variant, the temporal step Δt is also encoded into the bitstream B.

In one variant, multiple temporal steps Δt_r are encoded into the bitstream B. This variant is advantageous to denser sensing in some regions of interest r along the sensing path SP.

A sensing time t_s+1 in a region of interest r is then obtained by t_s+1=t_s+Δt_r.

According to this variant, the two-dimensional curve SP is segmented into time intervals TI according to regions of interest r.

In one variant, said time intervals TI may be encoded into the bitstream B.

In one variant, a time interval TI may be encoded relatively to at least one another time interval.

As a non-restrictive example, the time interval TI may be encoded relatively to the last encoded time interval. The time interval TI may also be encoded relatively to a combination of multiple previously encoded time intervals.

In one variant, the time interval TI or a residual derived from the time interval TI and at least one previously encoded time interval, may be compressed using an entropy coder like CABAC after binarization of their values.

Any other entropy coder may be used.

On the illustrative example of FIG. 16, a sensor head comprising two sensors is used. The sensing paths followed by the two sensors are represented in dash lines. For each sample index s (each sensing time instant), two coarse points are defined. The coarse points relative to the first sensor are represented by black shaded points on FIG. 16 and the coarse points relative to the second sensor are represented by a black hashed point. Each of these two coarse points belongs to a sensor sensing path (dash line) defined from the sensing path SP. FIG. 17 illustrates a representation of ordered coarse points in the two-dimensional coordinate (s,λ) space. Arrows on FIGS. 16 and 17 illustrate the links between two successive ordered coarse points.

An order index o(P) is associated with each coarse point according to the rank of said coarse point among the ordered coarse points.

In some embodiments, the order index o(P) of a coarse point occupied by a sensed point P of the point cloud is obtained by:

$o\left(P\right)=\lambda +s*K$

• • where K is the number of sensors of a set of sensors or the number of different positions of a single sensor for a same sample index, λ is the sensor index of the sensor that sensed the point P of the point cloud at the sensing time instant s.

In step 120, an information I(SP) representing the sensing path SP in the two-dimensional angular coordinate (ϕ, θ) space may be encoded into a bitstream B.

In some embodiments of step 120, the information I(SP) may comprise data representing the angular coordinates (ϕk, θk) of the control points ω_k.

In some embodiments of step 120, the angular coordinates (ϕ_k, θ_k) of the control points ω_k may be encoded directly into the bitstream B.

In some embodiments of step 120, the angular coordinates (ωk, θ_k) of the control points ω_k may be predictive encoded in the bitstream B. Predictive angular coordinates (ϕ_k,pred, θ_k,pred) may be obtained from angular coordinates of at least one previously encoded control point (ϕ_k−m, θ_k−m), and an angular residual (ϕ_k,res, θ_k,res) between the angular coordinates of the control point (ωk, θ_k) and the predicted angular coordinates (ϕ_k,pred, θ_k,pred) may be encoded into the bitstream B:

$\left({\varphi }_{k,\mathrm{res}},{\theta }_{k,\mathrm{res}}\right)=\left({\varphi }_k,{\theta }_k\right)-\left({\varphi }_{k,\mathrm{pred}},{\theta }_{k,\mathrm{pred}}\right)$

For example, the predicted angular coordinates may be obtained from the angular coordinates of the last encoded control point (m=1):

$\left({\varphi }_{k,\mathrm{pred}},{\theta }_{k,\mathrm{pred}}\right)=\left({\varphi }_{k-1},{\theta }_{k-1}\right)$

In another example, the predicted angular coordinates may be obtained by linearly combining multiple previously encoded control points (m=m1 to m2) where m1 and m2 are two integer values:

$\left({\varphi }_{k,\mathrm{pred}},{\theta }_{k,\mathrm{pred}}\right)=\sum _{m2}^{m=m1}{w}_m\left({\varphi }_{k-m},{\theta }_{k-m}\right)$

• • where w_m are weighting coefficients.

In one variant, the coordinates (ϕ_k, θ_k) or the angular residual (ϕ_k,res, θ_k,res) may be quantized before encoding.

In one variant, the angular residual (ϕ_k,res, θ_k,res) may be further compressed by using an entropy coder like CABAC (Context Adaptive Binary Arithmetic Coding) after binarization of their values.

Any other entropy coder may be used.

In some embodiments of step 120, when the sensing path SP does not vary with time, the information I(SP) may be encoded as an element of a High-Level Syntax (HLS), for example in a Sequence Parameter Set (SPS) that gathers general coding parameters for the point cloud. Then, the coded sensing path may be used for several instances of the point cloud (point cloud frames).

In some embodiments of step 120, when the sensing path SP is frame dependent, the information I(SP) may be encoded as a High-Level Syntax example in a Geometry Parameter Set (GPS) of a current point cloud frame. This requires the determination of the sensing path before coding in the GPS and may lead to some latency up to one frame.

In another embodiment of step 120, the information I(SP) may be encoded into the bitstream B carrying encoded point cloud data.

By doing so, coding on-the-fly the sensing path SP preserves the low latency advantage of the coarse representation as it requires only a local determination of the sensing path before coding.

Referring to FIG. 9, steps 130 to 190 run for each point P of the point cloud sensed by one of a sensor of a sensor head, each point P corresponding to a sensor index λ and sensing time instant s and thus to an occupied coarse point among the ordered coarse points.

A sensed point P of the point cloud may be represented by three-dimensional cartesian coordinates (x_p, y_p, z_p) or three-dimensional spherical coordinates (r_p, ϕ_p, θ_p). Well-known methods in prior art may be used to convert cartesian coordinates to spherical coordinates or inversely.

In step 130, one sample index s relative to a sensing time instant of a point P and one sensor index λ of a sensor that sensed the point P may be obtained.

The sample index s and the sensor index λ are obtained in a straightforward manner during sensing because the sensor index corresponds to the sensor used to sense the point P at a sensing time instant corresponding to the sample index s.

In step 140, one order index difference Δo is encoded into the bitstream B. The order index difference Δo represents a difference between an order index o(P) of a coarse point identified by the sample index s and the sensor index A, and an order index o (P₋₁) of another coarse point relative to a previous sensed point P₋₁ of the point cloud:

$\Delta o=o\left(P\right)-o\left(P_{-1}\right)$

In one variant, the order index difference Δo may be compressed using an entropy coder like CABAC after binarization of their values.

Any other entropy coder may be used.

In one variant, the order index o(P₁) of the first coarse point occupied by the first sensed point P₁ of the point cloud may be directly encoded into the bitstream B. This is equivalent to arbitrary setting the order index of a virtual zero-th point to zero, i.e., o(P₀)=0, and coding Δo₁=o(P₁)−o(P₀)=o(P₁).

Given the order index o(P₁) of the first coarse point occupied by the first sensed point P₁ of the point cloud, and order differences Δo, one can recursively reconstruct the order index o(P) of any coarse point occupied by a sensed point P of the point cloud by:

$o\left(P\right)=o\left(P_{-1}\right)+\Delta o$

On FIG. 18, five occupied coarse points are represented (black circles): two coarse points P_n and P_n+1 are occupied by two points of the point cloud sensed at a sensing time instant t₁ (corresponding to a sample index s₁) and three coarse points are occupied by three points of the point cloud sensed at a sensing time instant t₂ (corresponding to a sample index s₂). Assuming the coordinates of the first occupied coarse point P_n in the two-dimensional coordinate (s,λ) space are known beforehand, a first order index difference Δo_n+1 is obtained as a difference between the order index o(P_n+1) of the occupied coarse point P_n+1 and the order index o(P_n) of the occupied coarse point P_n. In the example Δo_n+1=2 because a coarse point is not occupied (white circle). A second order index difference Δo_n+2 is obtained as a difference between the order index o(P_n+2) of another occupied coarse point P_n+2 and the order index o(P_n+1) of the occupied coarse point P_n+1, and so on.

In step 150, one sensing point P_over(s) in the two-dimensional angular coordinate (ϕ, θ) space may be obtained from the sample index s according to the sensing scheme as above explained.

For example, the sensing scheme may inform that sensing is performed uniformly along the sensing path, such that the sensing P_over(s) is located at a distance (taking along the sensing path SP) proportional to the sample index s from some origin of the sensing path SP.

In step 160, a coarse point P_λ(s) in the two-dimensional angular coordinate space may be obtained from the sensing point P_over(s), the sample index s and the sensor index λ.

The obtained coarse point P_λ(s) is relative to the point P of the point cloud sensed by a sensor having a sensor index λ at a sensing time instant relative to the sample index s.

In some embodiments of step 160, illustrated on FIG. 19, the coarse point P_λ(s) may be obtained by shifting the sensing point P_over(s) in the two-dimensional angular coordinate (ϕ, θ) space:

$P_{\lambda }\left(s\right)=P_{\mathrm{over}}\left(s\right)+\delta P$

• • where δP is a shifting values vector for the sensing point P_over(s) that depends on the sensor index λ.

The angular coordinates (ϕ_λ, θ_λ) of the coarse point P_λ(s) may be obtained by:

$\left({\varphi }_{\lambda },{\theta }_{\lambda }\right)=\left({\varphi }_{o\mathrm{ver}},{\theta }_{\mathrm{over}}\right)+\left(\delta {\varphi }_{\lambda },{\mathrm{\delta \theta }}_{\lambda }\right)$

• • where δϕ_λ is an azimuthal angle shifting coefficient and δθ_λ is an elevation angle shifting coefficient both depending on the sensor index λ.

In step 170, the point P may be encoded into the bitstream B relatively to the coarse point P_λ(s).

In some embodiments of step 170, encoding the point P relatively to the coarse point P_λ(s) may comprise encoding an angular residual (res(ϕ), res(θ)) based on angular coordinates (ϕ_p, θ_p) of the point P and angular coordinates (ϕ_λ, θ_λ) of the coarse point P_λ(s):

$\left(\mathrm{res}\left(\varphi \right),\mathrm{res}\left(\theta \right)\right)=\left({\varphi }_P,{\theta }_P\right)-\left({\varphi }_{\lambda },{\theta }_{\lambda }\right)$

Optionally, the method 100 may further comprise in step 180, encoding, in the bitstream B, the radius value r_p of spherical or cylindrical coordinates of the point P. A reconstructed radius value r_p,rec may be obtained by decoding the encoded radius value r_p.

In some embodiments of step 180, the radius value r_p may be quantized and the reconstructed radius value r_p,rec may be obtained by inverse quantizing the quantized radius value Q1(r_p).

For example, Q1 is a uniform quantizer using a quantization step Δ1:

$Q1\left(r_p\right)=\mathrm{round}\left(\frac{r_p}{\Delta 1}\right)$

The reconstructed radius value r_p,rec is then obtained by inverse quantizing the quantized radius value Q1(r_p) by an inverse quantizer IQ1:

$\mathrm{IQ}1\left(Q1\left(r_p\right)\right)=Q1\left(r_p\right)*\Delta 1$

Any other quantizer and inverse quantizer may be used.

In an embodiment of step 180, the radius value r_p may be quantized and coded, and the reconstructed radius value r_p,rec is obtained by decoding and inverse quantizing the coded radius value r_p.

In one variant, the quantized radius value r_p may be entropy encoded using an entropy coder like CABAC after binarization of their values.

Any other entropy coder may be used.

In some embodiments of step 180, the radius value r_p may represent a three-dimensional radius r_3D.

In some embodiments of step 180, the radius value r_p may represent the projected radius r_2D on the horizontal xy plane as depicted on FIG. 4.

In some embodiments, the method 100 may further comprise, in step 190, encoding, into the bitstream B, a cartesian coordinate residual (x_p,res,y_p,res,z_p,res) for the point P based on the cartesian coordinates of the point P and cartesian coordinates (x_p,rec,y_p,rec,z_p,rec) of a reconstructed point P_rec obtained from the coarse point P_λ(s), the angular residual (res(ϕ), res(θ)) and the reconstructed radius value r_p,rec.

First, angular coordinates (ϕ_rec, θ_rec) of the reconstructed point P_rec are obtained by adding the angular coordinates (ϕ_λ, θ_λ) of the coarse point P_λ(s) and the angular residual (res(ϕ), res(θ)):

$\begin{array}{cc}\left({\varphi }_{\mathrm{rec}},{\theta }_{\mathrm{rec}}\right)=\left({\varphi }_{\lambda },{\theta }_{\lambda }\right)+\left(\mathrm{res}\left(\varphi \right),\mathrm{res}\left(\theta \right)\right)& \left(1\right)\end{array}$

Next, cartesian coordinates (x_p,rec,y_p,rec,z_p,rec) of the reconstructed point P_rec are obtained from the angular coordinates (ϕ_rec, θ_rec) of the reconstructed point P_rec and the radius value r_p,rec:

$\begin{array}{cc}\{\begin{array}{c}{x}_{p,\mathrm{rec}}=r_{p,\mathrm{rec}}*\cos \left({\varphi }_{\mathrm{rec}}\right)\\ y_{p,\mathrm{rec}}=r_{p,\mathrm{rec}}*\sin \left({\varphi }_{\mathrm{rec}}\right)\\ z_{p,\mathrm{rec}}=r_{p,\mathrm{rec}}*\tan \left({\theta }_{\mathrm{rec}}\right)\end{array}& \left(2\right)\end{array}$

Finally, the cartesian coordinate residual (x_p,res,y_p,res,z_p,res) is obtained based on the cartesian coordinates (x_p, y_p, z_p) of the point P and the cartesian coordinates (x_p,rec,y_p,rec,z_p,rec) of the reconstructed point P_rec:

$\begin{array}{cc}\left(x_{p,\mathrm{res}},y_{p,\mathrm{res}},z_{p,\mathrm{res}}\right)=\left(x_p,y_p,z_p\right)-\left(x_{p,\mathrm{rec}},y_{p,\mathrm{rec}},z_{p,\mathrm{rec}}\right)& \left(3\right)\end{array}$

In one variant, a quantized cartesian coordinate residual Q2(x_p,res,y_p,res,z_p,res) may be obtained by quantizing the cartesian coordinate residual (x_p,res,y_p,res,z_p,res) and the quantized cartesian coordinate residual Q2(x_p,res,y_p,res,z_p,res) is encoded.

For example, Q2 is a uniform quantizer using a quantization step Δ2:

$Q2\left(x_{p,\mathrm{res}},y_{p,\mathrm{res}},z_{p,\mathrm{res}}\right)=\mathrm{round}\left(\frac{\left(x_{p,\mathrm{res}},y_{p,\mathrm{res}},z_{p,\mathrm{res}}\right)}{\Delta 2}\right)$

The cartesian coordinate residual (x_p,res,y_p,res,z_p,res) may be obtained by inverse quantizing the quantized cartesian coordinate residual Q2(x_p,res,y_p,res,z_p,res) by:

$\mathrm{IQ}2\left(Q2\left(x_{p,\mathrm{res}},y_{p,\mathrm{res}},z_{p,\mathrm{res}}\right)\right)=Q2\left(x_{p,\mathrm{res}},y_{p,\mathrm{res}},z_{p,\mathrm{res}}\right)*\Delta 2$

Any other quantizer and inverse quantizer may be used.

In one variant, the quantized cartesian coordinate residual Q2(x_p,res,y_p,res,z_p,res) may be entropy encoded using an entropy coder like CABAC after binarization of their values.

Any other entropy coder may be used.

In one variant, a quantized angular residual Q3(res(ϕ), res(θ)) may be obtained by quantizing the angular residual (res(ϕ), res(θ)) and the quantized angular residual Q3(res(ϕ), res(θ)) is encoded.

Angular coordinates of the reconstructed point P_rec (equation 1) are then obtained by:

$\left({\varphi }_{\mathrm{rec}},{\theta }_{\mathrm{rec}}\right)=\left({\varphi }_{\lambda },{\theta }_{\lambda }\right)+\mathrm{IQ}3(Q3(\mathrm{res}\left(\varphi \right),$ $\mathrm{res}\left(\theta \right)))$

For example, Q3 is a uniform quantizer using a quantization step Δ3:

$Q3((\mathrm{res}\left(\varphi \right),$ $\mathrm{res}\left(\theta \right)))=\mathrm{round}\left(\frac{\left(\mathrm{res}\left(\varphi \right),\mathrm{res}\left(\theta \right)\right)}{\Delta 3}\right)$

And IQ3 is the inverse quantizer:

$\mathrm{IQ}3(Q3(\mathrm{res}\left(\varphi \right),$ $\mathrm{res}\left(\theta \right)))=Q3(\mathrm{res}\left(\varphi \right),$ $\mathrm{res}\left(\theta \right))*\Delta 3$

Any other quantizer and inverse quantizer may be used.

In one variant, the quantized angular residual Q3(res(ϕ), res(θ)) may be entropy encoded using an entropy coder like CABAC after binarization of their values.

Any other entropy encoder may be used.

FIG. 20 illustrates a schematic block diagram of steps of a method 200 of decoding a point cloud from a bitstream of encoded point cloud data in accordance with at least one embodiment.

Decoding points of a point cloud from a bitstream B requires information such as a number N of points of the point cloud. This information may be decoded from the bitstream B or received by any other means or may be known beforehand by a decoder.

In step 210, control points ω_k may be decoded from the bitstream B.

In some embodiments of step 210, the angular coordinates (ϕ_k, θ_k) of the control points ω_k may be decoded directly from the bitstream B.

In some embodiments of step 210, the angular coordinates (ϕ_k, θ_k) of the control points ω_k may be predictive decoded from the bitstream B.

In one variant, the coordinates (ϕ_k, θ_k) or the angular residual (ϕ_k,res, θ_k,res) may be decoded before inverse quantized.

In one variant, the angular residual (ϕ_k,res, θ_k,res) may be further decompressed by using an entropy decoder like CABAC after binarization of their values.

Any other entropy decoder may be used.

In step 210, the two-dimensional curve SP representing the sensing path may be obtained by interpolation between the control points ω_k. For example, a linear interpolation may be used. In another other example, higher order interpolation may be used like third order splines.

In some embodiments of step 210, when the sensing path SP does not vary with time, the information I(SP) may be decoded from an element of a High-Level Syntax (HLS), for example in a Sequence Parameter Set (SPS). Then, the decoded sensing path may be used for several instances of the point cloud (point cloud frames).

In some embodiments of step 210, when the sensing path SP is frame dependent, the information I(SP) may be decoded from a High-Level Syntax example in a Geometry Parameter Set (GPS) of a current point cloud frame. This requires the determination of the sensing path before decoding in the GPS and may lead to a latency up to one frame.

In another embodiment of step 210, the information I(SP) may be decoded from the bitstream B carrying encoded point cloud data.

By doing so, decoding on-the-fly the sensing path SP preserves the low latency advantage of the coarse representation as it requires only a local determination of the sensing path before coding.

Referring to FIG. 20, steps 220-260 and steps 150 and 160 run for each point P of the point cloud to be decoded.

In step 220, one order index difference Δo may be decoded from the bitstream B for a point P of the point cloud to be decoded.

The order index difference Δo represents a difference between an order index o(P) of a coarse point relative to the point P, and an order index o(P₋₁) of another coarse point relative to a previous decoded point P₋₁ of the point cloud.

In one variant, the order index o(P₁) of the first coarse point (occupied by the first point P₁ of the point cloud to be decoded) may be directly decoded from the bitstream B.

In one variant, the order index differences Δo_n may be decompressed using an entropy decoder like CABAC after binarization of their values.

Any other entropy decoder may be used.

In step 230, one sensor index λ associated with a sensor that sensed the point P and one sample index s relative to the sensing time instant of the point P may be obtained from an order index o(P) obtained from the decoded order index difference Δo.

The sensor index λ and the sample index s may be obtained from the order index o(P) by:

$\lambda =o\left(P\right)\mathrm{modulo}K$ $s=o\left(P\right)/K$

• • where the division/K is the integer division (aka Euclidian division). Therefore, o(P₁) and Δo are an alternative representation of λ and s.

In step 150, one sensing point P_over(s) in the two-dimensional angular coordinate (ϕ, θ) space may be obtained from the sample index s according to the sensing scheme as above discussed.

In step 160, a coarse point P_λ(s) in the two-dimensional angular coordinate space may be obtained from the sensing point P_over(s), the sample index s and the sensor index A as above explained.

In step 240, a point P of the point cloud may be decoded relatively to the coarse point P_λ(s).

In one embodiment of step 240, decoding a point P of the point cloud from a coarse point P_λ(s) may comprise decoding an angular residual (res(ϕ), res(θ)) and obtaining angular coordinates (ϕ_rec, θ_rec) of a reconstructed point P_rec by adding said decoded angular residual (res(ϕ), res(θ)) and angular coordinates (ϕ_λ, θ_λ) of the coarse point P_λ(s) (equation 1).

In step 270, the cartesian coordinates (x_p,y_p,z_p) of the point P may then be the cartesian coordinates (x_p,rec,y_p,rec,z_p,rec) obtained from the angular coordinates (ϕ_rec, θ_rec) of a reconstructed point P_rec and a radius value r_p,rec by equation (2).

Optionally, the method 200 may further comprise in step 250, decoding, from the bitstream B, the radius value r_p,rec.

In some embodiments of step 250, the radius value r_p,rec is entropy-decoded from the bitstream B.

In some embodiments of step 250, the radius value r_p,dec may be inverse quantized by the inverse quantizer IQ1. Any other inverse quantizer may be used.

In an embodiment of step 250, the radius value r_p,rec may be decoded and inverse quantized.

In one variant, the quantized radius value may be entropy decoded using an entropy coder like CABAC after binarization of their values.

Any other entropy coder may be used.

In some embodiments of step 250, the radius value r_p,rec represents a three-dimensional radius r_3D.

In some embodiments of step 250, the radius value r_p,rec represents the projected radius r_2D on the horizontal xy plane as depicted on FIG. 4.

In some embodiments, the method 200 further comprises in step 260, decoding a cartesian coordinate residual (x_p,res,y_p,res,z_p,res) from the bitstream B. The cartesian coordinate residual (x_p,res,y_p,res,z_p,res) is given by equation (3).

In step 270, the cartesian coordinates (x_p, y_p, z_p) of the point P may then be obtained by adding the decoded cartesian coordinate residual (x_p,res,y_p,res,z_p,res) and the cartesian coordinates of the reconstructed point P_rec:

$\left(x_p,y_p,z_p\right)=\left(x_{p,\mathrm{res}},y_{p,\mathrm{res}},z_{p,\mathrm{res}}\right)+\left(x_{p,\mathrm{rec}},y_{p,\mathrm{rec}},z_{p,\mathrm{rec}}\right)$

In one variant, the cartesian coordinate residual (x_p,res,y_p,res,z_p,res) may be obtained by decoding and inverse quantizing using the inverse quantizer IQ2.

Any other inverse quantizer may be used.

In one variant, the decoding may be an entropy decoder using an entropy decoder like CABAC after binarization of values.

Any other entropy decoder may be used.

In one variant, an angular residual (res(ϕ), res(θ)) may be obtained by decoding and inverse quantizing using the inverse quantizer Q3.

Any other quantizer and inverse quantizer may be used.

In one variant, the decoding may be an entropy decoder using an entropy decoder like CABAC after binarization of values.

Any other entropy decoder may be used.

Angular coordinates of the reconstructed point P_rec (equation 1) are then obtained by:

$\left({\varphi }_{\mathrm{rec}},{\theta }_{\mathrm{rec}}\right)=\left({\varphi }_{\lambda },{\theta }_{\lambda }\right)+\mathrm{IQ}3\left(Q3\left(\mathrm{res}\left(\varphi \right),\mathrm{res}\left(\theta \right)\right)\right)$

FIG. 21 shows a schematic block diagram illustrating an example of a system in which various aspects and embodiments are implemented.

System 300 may be embedded as one or more devices including the various components described below. In various embodiments, the system 300 may be configured to implement one or more of the aspects described in the present application.

Examples of equipment that may form all or part of the system 300 include personal computers, laptops, smartphones, tablet computers, digital multimedia set top boxes, digital television receivers, personal video recording systems, connected home appliances, connected vehicles and their associated processing systems, head mounted display devices (HMD, see-through glasses), projectors (beamers), “caves” (system including multiple displays), servers, video encoders, video decoders, post-processors processing output from a video decoder, pre-processors providing input to a video encoder, web servers, set-top boxes, and any other device for processing a point cloud, a video or an image or other communication devices. Elements of system 300, singly or in combination, may be embodied in a single integrated circuit (IC), multiple ICs, and/or discrete components. For example, in at least one embodiment, the processing and encoder/decoder elements of system 300 may be distributed across multiple ICs and/or discrete components. In various embodiments, the system 300 may be communicatively coupled to other similar systems, or to other electronic devices, via, for example, a communications bus or through dedicated input and/or output ports.

The system 300 may include at least one processor 310 configured to execute instructions loaded therein for implementing, for example, the various aspects described in the present application. Processor 310 may include embedded memory, input output interface, and various other circuitries as known in the art. The system 300 may include at least one memory 320 (for example a volatile memory device and/or a non-volatile memory device). System 300 may include a storage device 340, which may include non-volatile memory and/or volatile memory, including, but not limited to, Electrically Erasable Programmable Read-Only Memory (EEPROM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Random Access Memory (RAM), Dynamic Random-Access Memory (DRAM), Static Random-Access Memory (SRAM), flash, magnetic disk drive, and/or optical disk drive. The storage device 340 may include an internal storage device, an attached storage device, and/or a network accessible storage device, as non-limiting examples.

The system 300 may include an encoder/decoder module 330 configured, for example, to process data to provide encoded/decoded point cloud geometry data, and the encoder/decoder module 330 may include its own processor and memory. The encoder/decoder module 330 may represent module(s) that may be included in a device to perform the encoding and/or decoding functions. As is known, a device may include one or both encoding and decoding modules.

Additionally, encoder/decoder module 330 may be implemented as a separate element of system 300 or may be incorporated within processor 310 as a combination of hardware and software as known to those skilled in the art.

Program code to be loaded onto processor 310 or encoder/decoder module 330 to perform the various aspects described in the present application may be stored in storage device 340 and subsequently loaded onto memory 320 for execution by processor 310. In accordance with various embodiments, one or more of processor 310, memory 320, storage device 340, and encoder/decoder module 330 may store one or more of various items during the performance of the processes described in the present application. Such stored items may include, but are not limited to, a point cloud frame, encoded/decoded geometry/attributes videos/images or portions of the encoded/decoded geometry/attribute video/images, a bitstream, matrices, variables, and intermediate or final results from the processing of equations, formulas, operations, and operational logic.

In several embodiments, memory inside of the processor 310 and/or the encoder/decoder module 330 may be used to store instructions and to provide working memory for processing that may be performed during encoding or decoding.

In other embodiments, however, a memory external to the processing device (for example, the processing device may be either the processor 310 or the encoder/decoder module 330) may be used for one or more of these functions. The external memory may be the memory 320 and/or the storage device 340, for example, a dynamic volatile memory and/or a non-volatile flash memory. In several embodiments, an external non-volatile flash memory may be used to store the operating system of a television. In at least one embodiment, a fast external dynamic volatile memory such as a RAM may be used as working memory for video coding and decoding operations, such as for MPEG-2 part 2 (also known as ITU-T Recommendation H.262 and ISO/IEC 13818-2, also known as MPEG-2 Video), HEVC (High Efficiency Video coding), VVC (Versatile Video Coding), or MPEG-I part 5 or part 9.

The input to the elements of system 300 may be provided through various input devices as indicated in block 390. Such input devices include, but are not limited to, (i) an RF portion that may receive an RF signal transmitted, for example, over the air by a broadcaster, (ii) a Composite input terminal, (iii) a USB input terminal, and/or (iv) an HDMI input terminal.

In various embodiments, the input devices of block 390 may have associated respective input processing elements as known in the art. For example, the RF portion may be associated with elements necessary for (i) selecting a desired frequency (also referred to as selecting a signal, or band-limiting a signal to a band of frequencies), (ii) down-converting the selected signal, (iii) band-limiting again to a narrower band of frequencies to select (for example) a signal frequency band which may be referred to as a channel in certain embodiments, (iv) demodulating the down-converted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets. The RF portion of various embodiments may include one or more elements to perform these functions, for example, frequency selectors, signal selectors, band-limiters, channel selectors, filters, downconverters, demodulators, error correctors, and de-multiplexers. The RF portion may include a tuner that performs various of these functions, including, for example, down-converting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or to baseband.

In one set-top box embodiment, the RF portion and its associated input processing element may receive an RF signal transmitted over a wired (for example, cable) medium. Then, the RF portion may perform frequency selection by filtering, down-converting, and filtering again to a desired frequency band.

Various embodiments rearrange the order of the above-described (and other) elements, remove some of these elements, and/or add other elements performing similar or different functions.

Adding elements may include inserting elements in between existing elements, such as, for example, inserting amplifiers and an analog-to-digital converter. In various embodiments, the RF portion may include an antenna.

Additionally, the USB and/or HDMI terminals may include respective interface processors for connecting system 300 to other electronic devices across USB and/or HDMI connections. It is to be understood that various aspects of input processing, for example, Reed-Solomon error correction, may be implemented, for example, within a separate input processing IC or within processor 310 as necessary. Similarly, aspects of USB or HDMI interface processing may be implemented within separate interface ICs or within processor 310 as necessary. The demodulated, error corrected, and demultiplexed stream may be provided to various processing elements, including, for example, processor 310, and encoder/decoder module 330 operating in combination with the memory and storage elements to process the data stream as necessary for presentation on an output device.

Various elements of system 300 may be provided within an integrated housing. Within the integrated housing, the various elements may be interconnected and transmit data therebetween using a suitable connection arrangement in block 390, for example, an internal bus as known in the art, including the I2C bus, wiring, and printed circuit boards.

The system 300 may include communication interface 350 that enables communication with other devices via communication channel 700. The communication interface 350 may include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 700. The communication interface 350 may include, but is not limited to, a modem or network card and the communication channel 700 may be implemented, for example, within a wired and/or a wireless medium.

Data may be streamed to the system 300, in various embodiments, using a Wi-Fi network such as IEEE 802.11. The Wi-Fi signal of these embodiments may be received over the communications channel 700 and the communications interface 350 which are adapted for Wi-Fi communications. The communications channel 700 of these embodiments may be typically connected to an access point or router that provides access to outside networks including the Internet for allowing streaming applications and other over-the-top communications.

Other embodiments may provide streamed data to the system 300 using a set-top box that delivers the data over the HDMI connection of the input block 390.

Still other embodiments may provide streamed data to the system 300 using the RF connection of the input block 390.

The streamed data may be used as a way for signaling information used by the system 300. The signaling information may comprise the bitstream B and/or information such a number of points of a point cloud, coordinates, and/or sensor setup parameters.

It is to be appreciated that signaling may be accomplished in a variety of ways. For example, one or more syntax elements, flags, and so forth may be used to signal information to a corresponding decoder in various embodiments.

The system 300 may provide an output signal to various output devices, including a display 400, speakers 500, and other peripheral devices 600. The other peripheral devices 600 may include, in various examples of embodiments, one or more of a stand-alone DVR, a disk player, a stereo system, a lighting system, and other devices that provide a function based on the output of the system 300.

In various embodiments, control signals may be communicated between the system 300 and the display 400, speakers 500, or other peripheral devices 600 using signaling such as AV.Link (Audio/Video Link), CEC (Consumer Electronics Control), or other communications protocols that enable device-to-device control with or without user intervention.

The output devices may be communicatively coupled to system 300 via dedicated connections through respective interfaces 360, 370, and 380.

Alternatively, the output devices may be connected to system 300 using the communications channel 700 via the communications interface 350. The display 400 and speakers 500 may be integrated in a single unit with the other components of system 300 in an electronic device such as, for example, a television.

In various embodiments, the display interface 360 may include a display driver, such as, for example, a timing controller (T Con) chip.

The display 400 and speaker 500 may alternatively be separate from one or more of the other components, for example, if the RF portion of block 390 is part of a separate set-top box. In various embodiments in which the display 400 and speakers 500 may be external components, the output signal may be provided via dedicated output connections, including, for example, HDMI ports, USB ports, or COMP outputs.

In FIG. 1-21, various methods are described herein, and each of the methods includes one or more steps or actions for achieving the described method. Unless a specific order of steps or actions is required for proper operation of the method, the order and/or use of specific steps and/or actions may be modified or combined.

Some examples are described with regard to block diagrams and/or operational flowcharts. Each block represents a circuit element, module, or portion of code which includes one or more executable instructions for implementing the specified logical function(s). It should also be noted that in other implementations, the function(s) noted in the blocks may occur out of the indicated order. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending on the functionality involved.

The implementations and aspects described herein may be implemented in, for example, a method or a process, an apparatus, a computer program, a data stream, a bitstream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed may also be implemented in other forms (for example, an apparatus or computer program).

The methods may be implemented in, for example, a processor, which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processors also include communication devices.

Additionally, the methods may be implemented by instructions being performed by a processor, and such instructions (and/or data values produced by an implementation) may be stored on a computer readable storage medium. A computer readable storage medium may take the form of a computer readable program product embodied in one or more computer readable medium(s) and having computer readable program code embodied thereon that is executable by a computer. A computer readable storage medium as used herein may be considered a non-transitory storage medium given the inherent capability to store the information therein as well as the inherent capability to provide retrieval of the information therefrom. A computer readable storage medium may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. It is to be appreciated that the following, while providing more specific examples of computer readable storage mediums to which the present embodiments may be applied, is merely an illustrative and not an exhaustive listing as is readily appreciated by one of ordinary skill in the art: a portable computer diskette; a hard disk; a read-only memory (ROM); an erasable programmable read-only memory (EPROM or Flash memory); a portable compact disc read-only memory (CD-ROM); an optical storage device; a magnetic storage device; or any suitable combination of the foregoing.

The instructions may form an application program tangibly embodied on a processor-readable medium.

Instructions may be, for example, in hardware, firmware, software, or a combination. Instructions may be found in, for example, an operating system, a separate application, or a combination of the two. A processor may be characterized, therefore, as, for example, both a device configured to carry out a process and a device that includes a processor-readable medium (such as a storage device) having instructions for carrying out a process. Further, a processor-readable medium may store, in addition to or in lieu of instructions, data values produced by an implementation.

An apparatus may be implemented in, for example, appropriate hardware, software, and firmware. Examples of such apparatus include personal computers, laptops, smartphones, tablet computers, digital multimedia set top boxes, digital television receivers, personal video recording systems, connected home appliances, head mounted display devices (HMD, see-through glasses), projectors (beamers), “caves” (system including multiple displays), servers, video encoders, video decoders, post-processors processing output from a video decoder, pre-processors providing input to a video encoder, web servers, set-top boxes, and any other device for processing a point cloud, a video or an image or other communication devices. As should be clear, the equipment may be mobile and even installed in a mobile vehicle.

Computer software may be implemented by the processor 310 or by hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments may be also implemented by one or more integrated circuits. The memory 320 may be of any type appropriate to the technical environment and may be implemented using any appropriate data storage technology, such as optical memory devices, magnetic memory devices, semiconductor-based memory devices, fixed memory, and removable memory, as non-limiting examples. The processor 310 may be of any type appropriate to the technical environment, and may encompass one or more of microprocessors, general purpose computers, special purpose computers, and processors based on a multi-core architecture, as non-limiting examples.

As will be evident to one of ordinary skill in the art, implementations may produce a variety of signals formatted to carry information that may be, for example, stored or transmitted. The information may include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal may be formatted to carry the bitstream of a described embodiment. Such a signal may be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal. The formatting may include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information that the signal carries may be, for example, analog or digital information. The signal may be transmitted over a variety of different wired or wireless links, as is known. The signal may be stored on a processor-readable medium.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms “a”, “an”, and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “includes/comprises” and/or “including/comprising” when used in this specification, may specify the presence of stated, for example, features, integers, steps, operations, elements, and/or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Moreover, when an element is referred to as being “responsive” or “connected” to another element, it may be directly responsive or connected to the other element, or intervening elements may be present. In contrast, when an element is referred to as being “directly responsive” or “directly connected” to other element, there are no intervening elements present.

It is to be appreciated that the use of any of the symbol/term “/”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, may be intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended, as is clear to one of ordinary skill in this and related arts, for as many items as are listed.

Various numeric values may be used in the present application. The specific values may be for example purposes and the aspects described are not limited to these specific values.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the teachings of this application. No ordering is implied between a first element and a second element.

Reference to “one embodiment” or “an embodiment” or “one implementation” or “an implementation”, as well as other variations thereof, is frequently used to convey that a particular feature, structure, characteristic, and so forth (described in connection with the embodiment/implementation) is included in at least one embodiment/implementation. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment” or “in one implementation” or “in an implementation”, as well any other variations, appearing in various places throughout this application are not necessarily all referring to the same embodiment. Similarly, reference herein to “in accordance with an embodiment/example/implementation” or “in an embodiment/example/implementation”, as well as other variations thereof, is frequently used to convey that a particular feature, structure, or characteristic (described in connection with the embodiment/example/implementation) may be included in at least one embodiment/example/implementation. Thus, the appearances of the expression “in accordance with an embodiment/example/implementation” or “in an embodiment/example/implementation” in various places in the specification are not necessarily all referring to the same embodiment/example/implementation, nor are separate or alternative embodiment/examples/implementation necessarily mutually exclusive of other embodiments/examples/implementation.

Reference numerals appearing in the claims are by way of illustration only and shall have no limiting effect on the scope of the claims. Although not explicitly described, the present embodiments/examples and variants may be employed in any combination or sub-combination.

When a figure is presented as a flow diagram, it should be understood that it also provides a block diagram of a corresponding apparatus. Similarly, when a figure is presented as a block diagram, it should be understood that it also provides a flow diagram of a corresponding method/process.

Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

Various implementations involve decoding. “Decoding”, as used in this application, may encompass all or part of the processes performed, for example, on a received point cloud frame (including possibly a received bitstream which encodes one or more point cloud frames) in order to produce a final output suitable for display or for further processing in the reconstructed point cloud domain. In various embodiments, such processes include one or more of the processes typically performed by a decoder. In various embodiments, such processes also, or alternatively, include processes performed by a decoder of various implementations described in this application, for example.

As further examples, in one embodiment “decoding” may refer only to de-quantizing, in one embodiment “decoding” may refer to entropy decoding, in another embodiment “decoding” may refer only to differential decoding, and in another embodiment “decoding” may refer to combinations of de-quantizing, entropy decoding and differential decoding. Whether the phrase “decoding process” may be intended to refer specifically to a subset of operations or generally to the broader decoding process will be clear based on the context of the specific descriptions and is believed to be well understood by those skilled in the art.

Various implementations involve encoding. In an analogous way to the above discussion about “decoding”, “encoding” as used in this application may encompass all or part of the processes performed, for example, on an input point cloud frame in order to produce an encoded bitstream. In various embodiments, such processes include one or more of the processes typically performed by an encoder. In various embodiments, such processes also, or alternatively, include processes performed by an encoder of various implementations described in this application.

As further examples, in one embodiment “encoding” may refer only to quantizing, in one embodiment “encoding” may refer only to entropy encoding, in another embodiment “encoding” may refer only to differential encoding, and in another embodiment “encoding” may refer to combinations of quantizing, differential encoding and entropy encoding. Whether the phrase “encoding process” may be intended to refer specifically to a subset of operations or generally to the broader encoding process will be clear based on the context of the specific descriptions and is believed to be well understood by those skilled in the art.

Additionally, this application may refer to “obtaining” various pieces of information. Obtaining the information may include one or more of, for example, estimating the information, calculating the information, predicting the information, or retrieving the information from memory.

Further, this application may refer to “accessing” various pieces of information. Accessing the information may include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, moving the information, copying the information, calculating the information, determining the information, predicting the information, or estimating the information.

Additionally, this application may refer to “receiving” various pieces of information. Receiving is, as with “accessing”, intended to be a broad term. Receiving the information may include one or more of, for example, accessing the information, or retrieving the information (for example, from memory). Further, “receiving” is typically involved, in one way or another, during operations such as, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.

Also, as used herein, the word “signal” refers to, among other things, indicating something to a corresponding decoder. For example, in certain embodiments the encoder signals a particular information such a number of points of the point cloud or coordinates or sensor setup parameters. In this way, in an embodiment the same parameter may be used at both the encoder side and the decoder side. Thus, for example, an encoder may transmit (explicit signaling) a particular parameter to the decoder so that the decoder may use the same particular parameter.

Conversely, if the decoder already has the particular parameter as well as others, then signaling may be used without transmitting (implicit signaling) to simply allow the decoder to know and select the particular parameter. By avoiding transmission of any actual functions, a bit savings is realized in various embodiments. It is to be appreciated that signaling may be accomplished in a variety of ways. For example, one or more syntax elements, flags, and so forth are used to signal information to a corresponding decoder in various embodiments. While the preceding relates to the verb form of the word “signal”, the word “signal” may also be used herein as a noun.

A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. For example, elements of different implementations may be combined, supplemented, modified, or removed to produce other implementations. Additionally, one of ordinary skill will understand that other structures and processes may be substituted for those disclosed and the resulting implementations will perform at least substantially the same function(s), in at least substantially the same way(s), to achieve at least substantially the same result(s) as the implementations disclosed. Accordingly, these and other implementations are contemplated by this application.

Claims

1. A method of encoding points (P) of a point cloud into a bitstream of encoded point cloud data, wherein the points (P) of the point cloud are sensed by at least one sensor according to a sensing path represented by a two-dimensional curve (SP) in a two-dimensional angular coordinate space comprising an azimuthal coordinate representative of an azimuthal angle representing a sensing angle of a sensor relative to a referential and an elevation coordinate representative of an elevation angle of a sensor relative to a horizontal referential plane;and wherein the method comprises:encoding, into the bitstream, control points (ωk) used to obtain the two-dimensional curve (SP) by interpolation between said control points; andencoding, into the bitstream, the points (P) of the point cloud relatively to coarse points (Pλ(s)) coarsely representing angular coordinates of the points of the point cloud, a coarse point being defined in the two-dimensional angular coordinate space from a sample index relative to a sensing time instant along the two-dimensional curve and one sensor index associated with a sensor.

2. (canceled)

3. The method of claim 1, wherein sensing time instants are regularly spaced along the two-dimensional curve.

4. The method of claim 1, wherein the two-dimensional curve comprises a same number of sensing time instants between each pair of control points, and a sensing time instant corresponds to each control point.

5. The method of 1, wherein the two-dimensional curve is parametrized by associating a sampling index (sk) with each control point.

6. The method of claim 1, wherein the two-dimensional curve is parametrized by associating a timestamp parameter with each control point, and a sensing time instant is obtained from a previous sensing time instant and a temporal step.

7. The method of claim 6, wherein multiple temporal steps are used to obtain sensing time instants.

8. The method of claim 5, wherein the sampling indices (sk) associated with the control points, or the timestamp parameters (tk) associated with the control points and a temporal step or multiple temporal steps is/are encoded into the bitstream together with angular coordinates (ϕk, θk) of the control points.

9. An apparatus of encoding points of a point cloud into a bitstream of encoded point cloud data, wherein the points of the point cloud are sensed by at least one sensor according to a sensing path represented by a two-dimensional curve (SP) in a two-dimensional angular coordinate space comprising an azimuthal coordinate representative of an azimuthal angle representing a sensing angle of a sensor relative to a referential and an elevation coordinate representative of an elevation angle of a sensor relative to a horizontal referential plane;and wherein the apparatus comprises at least one processor configured to:encode, into the bitstream, control points (ωk) used to obtain the two-dimensional curve (SP) by interpolation between said control points; andencode, into the bitstream, the points (P) of the point cloud relatively to coarse points (Pλ(s)) coarsely representing angular coordinates of the points of the point cloud, a coarse point being defined in the two-dimensional angular coordinate space from a sample index relative to a sensing time instant along the two-dimensional curve and one sensor index associated with a sensor.

10. An apparatus of decoding points of a point cloud from a bitstream of encoded point cloud data, wherein the apparatus comprises at least one processor configured to perform the method of claim 16.

11-12. (canceled)

13. A non-transitory storage medium carrying instructions of program code for executing the method of claim 1.

14. (canceled)

15. A non-transitory storage medium carrying instructions of program code for executing the method of claim 16.

16. The method of claim 3, wherein the two-dimensional curve comprises a same number of sensing time instants between each pair of control points, and a sensing time instant corresponds to each control point.

17. A method of decoding points (P) of a point cloud from a bitstream of encoded point cloud data, wherein the method comprises: decoding control points (ωk) from the bitstream, the control points being used to obtain a two-dimensional curve (SP) representing a sensing path in a two-dimensional curve in a two-dimensional angular coordinate space comprising an azimuthal coordinate representative of an azimuthal angle representing a sensing angle of a sensor relative to a referential and an elevation coordinate representative of an elevation angle of a sensor relative to a horizontal referential plane;decoding, from the bitstream, the points (P) of the point cloud relatively to coarse points (Pλ(s)) having angular coordinates in the two-dimensional angular coordinate space that coarsely represent angular coordinates of the points of the point cloud, a coarse point being defined in the two-dimensional angular coordinate space from a sample index relative to a sensing time instant along the two-dimensional curve and one sensor index associated with a sensor.

18. The method of claim 17, wherein sensing time instants are regularly spaced along the two-dimensional curve.

19. The method of claim 17, wherein the two-dimensional curve comprises a same number of sensing time instants between each pair of control points, and a sensing time instant corresponds to each control point.

20. The method of claim 18, wherein the two-dimensional curve comprises a same number of sensing time instants between each pair of control points, and a sensing time instant corresponds to each control point.

21. The method of claim 17, wherein the two-dimensional curve is parametrized by associating a sampling index (sk) with each control point.

22. The method of claim 17, wherein the two-dimensional curve is parametrized by associating a timestamp parameter with each control point, and a sensing time instant is obtained from a previous sensing time instant and a temporal step.

23. The method of claim 22, wherein multiple temporal steps are used to obtain sensing time instants.

24. The method of claim 21, wherein the sampling indices (sk) associated with the control points, or the timestamp parameters (tk) associated with the control points and a temporal step or multiple temporal steps is/are encoded into the bitstream together with angular coordinates (ϕk, θk) of the control points.