This invention relates to a user interaction system having a totem that defines a six degree of freedom (“6dof”) pose, or pose, of a virtual object that is perceived by a user.
Modern computing and display technology has facilitated the development of user interaction systems that include “augmented reality” viewing devices. Such a viewing device usually has a head unit with a head unit body that is mountable to a head of a user and frequently includes two waveguides, one in front of each eye of the user. The waveguides are transparent so that ambient light from real-world objects can transmit through the waveguides and the user can see the real-world objects. Each waveguide also serves to transmit projected light from a projector to a respective eye of the user. The projected light forms an image on the retina of the eye. The retina of the eye thus receives the ambient light and the projected light. The user simultaneously sees real-world objects and one or more virtual objects that are created by the projected light.
Such a user interaction system often includes a totem. The user may, for example, hold the totem in their right hand and move the totem with six degrees of freedom in three-dimensional space. A virtual object may be perceived by the user to be attached to the totem and move with the totem in three-dimensional space, or the virtual object may be the perception of a light beam that hits the wall or another object that the user moves across the wall.
It is important for the virtual object to remain in its realistic pose relative to the totem. For example, if the totem represents the handle of a racket and the virtual object represents a head of the racket, the head of the racket has to remain “attached” to the handle of the racket over time.
The invention provides a user interaction system including a totem having a totem body, an electromagnetic (EM) transmitter on the totem body and a totem inertial measurement unit (IMU) located on the totem, to generate a totem IMU signal due to movement of the totem; a head unit having a head unit body and an EM receiver on the head unit body to receive an EM wave transmitted by the EM transmitter, the EM wave being indicative of a location of the totem; a processor; a storage device connected to the processor and a set of instructions on the storage device and executable by the processor. The set of instructions includes a world frame, a fusion routine connected to the EM receiver and the totem IMU to generate a fused pose of the totem in the world frame based on a combination of the EM wave, the head unit pose, and the totem IMU data, an unfused pose determination modeler that determines a pose of the totem relative to the head unit and a pose of the head unit relative to the world frame to establish an unfused pose of the totem relative to the world frame, a comparator connected to the fused pose determination modeler and the unfused pose determination modeler to compare the fused pose with the unfused pose, a drift declarer connected to the comparator to declare a drift only if the fused pose is more than a predetermined distance from the unfused pose, a location correction routine connected to the drift declarer to reset a pose of the totem IMU to match the unfused location only if the drift is declared, a data source to carry image data and a display system connected to the data source to display a virtual object using the image data to a user, a location of the virtual object being based on the fused location of the totem.
The invention also provides a user interaction system including transmitting an electromagnetic (EM) wave with an EM transmitter on a totem body, generating a totem inertial measurement unit (IMU) signal with a totem IMU on the totem body due to movement of the totem, locating a head unit body on a head of a user, receiving the EM wave transmitted by the EM transmitter by an EM receiver on the head unit body, the EM wave being indicative of a pose of the totem, storing a world frame, executing, with a processor, a fusion routine to generate a fused pose of the totem in the world frame based on a combination of the EM wave, head unit pose, and the totem IMU data, executing, with the processor, an unfused pose determination modeler that determines a pose of the totem relative to the head unit and a location of the head unit relative to the world frame to establish an unfused pose of the totem relative to the world frame, executing, with the processor, a comparator to compare the fused pose with the unfused pose, executing, with the processor, a drift declarer to declare a drift only if the fused pose is more than a predetermined pose from the unfused pose, executing, with the processor, a pose correction routine to reset pose of the totem IMU to match the unfused pose only if the drift is declared, receiving image data from a data source; and displaying, with a display system connected to the data source, a virtual object using the image data to a user, a location of the virtual object being based on the fused location of the totem.
The invention is further described by way of example with reference to the accompanying drawings, wherein:
The user interaction system 12 includes a head unit 18, a belt pack 20, a network 22 and a server 24.
The head unit 18 includes a head unit body 26 and a display system 28. The head unit body 26 has a shape that fits over a head of the user 10. The display system 28 is secured to the head unit body 26.
The belt pack 20 has a processor and a storage device connected to the processor. Vision algorithms are stored on the storage device and are executable by the processor. The belt pack 20 is communicatively connected to the display system 28 with a cable connection 30. The belt pack 20 further includes a network interface device that permits the belt pack 20 to connect wirelessly over a link 32 with the network 22. The server 24 is connected to the network 22.
In use, the user 10 secures the head unit body 26 to their head. The display system 28 includes an optical waveguide (not shown) that is transparent so that the user 10 can see the real-world object 14 through the waveguide.
The belt pack 20 may download image data from the server 24 over the network 22 and the link 32. The belt pack 20 provides the image data through the cable connection 30 to the display system 28. The display system 28 has one or more projectors that create light based on the image data. The light propagates through the one or more optical waveguides to eyes of the user 10. Each waveguide creates light at a particular focal length on a retina of a respective eye so that the eye sees the virtual object 16 at some distance behind the display system 28. The eye thus sees the virtual object 16 in three-dimensional space. Additionally, slightly different images are created for each eye so that a brain of the user 10 perceives the virtual object 16 in three-dimensional space. The user 10 thus sees the real-world object 14 augmented with the virtual object 16 in three-dimensional space.
The user interaction system 12 further includes a totem 34. In use, the user 10 holds the totem 34 in one of their hands. The virtual object 16 is positioned in three-dimensional space based on the positioning of the totem 34. By way of example, the totem 34 may be a handle of a racket and the virtual object 16 may include the head of the racket. The user 10 can move the totem 34 in six degrees of freedom in three-dimensional space. The totem 34 thus moves in three-dimensional space relative to the real-world object 14 and the head unit body 26. Various components within the head unit 18 and the belt pack 20 track movement of the totem 34 and move the virtual object 16 together with the totem 34. The head of the racket thus remains attached to the handle in the view of the user 10.
The vision algorithms 38 include a render engine 42, a stereoscopic analyzer 44, a display adjustment algorithm 46 and a simultaneous localization and mapping (SLAM) system 48.
The render engine 42 is connected to the data source 40 and the display adjustment algorithm 46. The render engine 42 is capable of receiving inputs from various systems, in the present example the display adjustment algorithm 46, and positions the image data within a frame that is to be viewed by the user 10 based on the display adjustment algorithm 46. The display adjustment algorithm 46 is connected to the SLAM system 48. The SLAM system 48 is capable of receiving image data, analyzing the image data for purposes of determining objects within images of the image data, and recording the locations of the objects within the image data.
The stereoscopic analyzer 44 is connected to the render engine 42. The stereoscopic analyzer 44 is capable of determining left and right image data sets from a data stream that is provided by the render engine 42.
The display system 28 includes left and right projectors 48A and 48B, left and right waveguides 50A and 50B, and detection devices 52. The left and right projectors 48A and 48B are connected to power supplies. Each projector 48A or 48B has a respective input for image data to be provided to the respective projector 48A or 48B. The respective projector 48A or 48B, when powered, generates light in a two-dimensional pattern and emanates the light therefrom. The left and right waveguides 50A and 50B are positioned to receive the light from the left and right projectors 48A and 48B, respectively. The left and right waveguides 50A and 50B are transparent waveguides.
The detection devices 52 include a head unit inertial motion unit (IMU) 60 and one or more head unit cameras 62. The head unit IMU 60 includes one or more gyroscopes and one or more accelerometers. The gyroscopes and accelerometers are typically formed in a semiconductor chip and are capable of detecting movement of the head unit IMU 60 and the head unit body 26, including movement along three orthogonal axes and rotation about three orthogonal axes.
The head unit cameras 62 continually capture images from an environment around the head unit body 26. The images can be compared to one another to detect movement of the head unit body 26 and the head of the user 10.
The SLAM system 48 is connected to the head unit cameras 62. The display adjustment algorithm 46 is connected to the head unit IMU 60. One of ordinary skill in the art will appreciate that the connections between the detection devices 52 and the vision algorithms 38 are accomplished through a combination of hardware, firmware and software. The components of the vision algorithms 38 are linked to one another through subroutines or calls.
In use, the user 10 mounts the head unit body 26 to their head. Components of the head unit body 26 may, for example, include a strap (not shown) that wraps around the back of a head of the user 10. The left and right waveguides 50A and 50B are then located in front of left and right eyes 120A and 120B of the user 10.
The render engine 42 receives image data from the data source 40. The render engine 42 enters the image data into the stereoscopic analyzer 44. The image data is three-dimensional image data of the virtual object 16 in
The stereoscopic analyzer 44 enters the left and right image data sets into the left and right projectors 48A and 48B. The left and right projectors 48A and 48B then create left and right light patterns. The components of the display system 28 are shown in plan view, although it should be understood that the left and right patters are two-dimensional patterns when shown in front elevation view. Each light pattern includes a plurality of pixels. For purposes of illustration, light rays 124A and 126A from two of the pixels are shown leaving the left projector 48A and entering the left waveguide 50A. The light rays 124A and 126A reflect from sides of the left waveguide 50A. It is shown that the light rays 124A and 126A propagate through internal reflection from left to right within the left waveguide 50A, although it should be understood that the light rays 124A and 126A also propagate in a direction into the paper using refractory and reflective systems.
The light rays 124A and 126A exit the left light waveguide 50A through a pupil 128A and then enter a left eye 120A through a pupil 130A of the left eye 120A. The light rays 124A and 126A then fall on a retina 132A of the left eye 120A. In this manner, the left light pattern falls on the retina 132A of the left eye 120A. The user 10 is given the perception that the pixels that are formed on the retina 132A are pixels 134A and 136A that the user 10 perceives to be at some distance on a side of the left waveguide 50A opposing the left eye 120A. Depth perception is created by manipulating the focal length of the light.
In a similar manner, the stereoscopic analyzer 44 enters the right image data set into the right projector 48B. The right projector 48B transmits the right light pattern, which is represented by pixels in the form of light rays 124B and 126B. The light rays 124B and 126B reflect within the right waveguide 50B and exit through a pupil 128B. The light rays 124B and 126B then enter through a pupil 130B of the right eye 120B and fall on a retina 132B of a right eye 120B. The pixels of the light rays 124B and 126B are perceived as pixels 134B and 136B behind the right waveguide 50B.
The patterns that are created on the retinas 132A and 132B are individually perceived as left and right images. The left and right images differ slightly from one another due to the functioning of the stereoscopic analyzer 44. The left and right images are perceived in a mind of the user 10 as a three-dimensional rendering.
As mentioned, the left and right waveguides 50A and 50B are transparent. Light from a real-life object on a side of the left and right waveguides 50A and 50B opposing the eyes 120A and 120B can project through the left and right waveguides 50A and 50B and fall on the retinas 132A and 132B. In particular, light from the real-world object 14 in
The head unit IMU 60 detects every movement of the head of the user 10. Should the user 10, for example, move their head counterclockwise and simultaneously move their body together with their head towards the right, such movement will be detected by the gyroscopes and accelerometers in the head unit IMU 60. The head unit IMU 60 provides the measurements from the gyroscopes and the accelerometers to the display adjustment algorithm 46. The display adjustment algorithm 46 calculates a placement value and provides the placement value to the render engine 42. The render engine 42 modifies the image data received from the data source 40 to compensate for the movement of the head of the user 10. The render engine 42 provides the modified image data to the stereoscopic analyzer 44 for display to the user 10.
The head unit cameras 62 continually capture images as the user 10 moves their head. The SLAM system 48 analyzes the images and identifies images of objects within the image. The SLAM system 48 analyzes movement of the objects to determine a pose position of the head unit body 26. The SLAM system 48 provides the pose position to the display adjustment algorithm 46. The display adjustment algorithm 46 uses the pose position to further refine the placement value that the display adjustment algorithm 46 provides to the render engine 42. The render engine 42 thus modifies the image data received from the data source 40 based on a combination of the motion sensors in the head unit IMU 60 and images taken by the head unit cameras 62. By way of a practical example, if the user 10 rotates their head to the right, a location of the virtual object 16 rotates to the left within the view of the user 10 thus giving the user 10 the impression that the location of the virtual object 16 remains stationary relative to the real-world object 14 and the totem 34.
The totem 34 has a totem body 152, an EM transmitter 154 and a totem IMU 156. The EM transmitter 154 and the totem IMU 156 are mounted in fixed positions relative to the totem body 152. The user 10 holds on to the totem body 152 and when the user 10 moves the totem body 152, the EM transmitter 154 and the totem IMU 156 move together with the totem body 152. The EM transmitter 154 is capable of transmitting an EM wave and the EM receiver 150 is capable of receiving the EM wave. The totem IMU 156 has one or more gyroscopes and one or more accelerometers. The gyroscopes and accelerometers are typically formed in a semiconductor chip and are capable of detecting movement of the totem IMU 156 and the totem body 152, including movement along three orthogonal axes and rotation about three orthogonal axes.
The vision algorithms 38, in addition to the data source 40, render engine 42, stereoscopic analyzer 44 and SLAM system 48 described with reference to
The head unit cameras 62 capture images of the real-world object 14. The images of the real-world object 14 are processed by the SLAM system 48 to establish a world frame 172 as described with reference to
The EM transmitter 154 transmits an EM wave that is received by the EM receiver 150. The EM wave that is received by the EM receiver 150 indicates a pose or a change of a pose of the EM transmitter 154. The EM receiver 150 enters data of the EM wave into the fusion routine 160.
The totem IMU 156 continually monitors movement of the totem body 152. Data from the totem IMU 156 is entered into the fusion routine 160.
The sequencer 170 executes the fusion routine 160 at a frequency of 250 Hz. The fusion routine 160 combines the data from the EM receiver 150 with the data from the totem IMU 156 and from the SLAM system 48. The EM wave that is received by the EM receiver 150 includes data that represents relatively accurately the pose of the EM transmitter 154 relative to the EM receiver 150 in six degrees of freedom (“6dof”). However, due to EM measurement noise, the measured EM wave may not accurately represent the pose of the EM transmitter 154 relative to the EM receiver 150. The EM measurement noise may result in jitter of the virtual object 16 in
As shown in
The totem IMU 156 essentially measures acceleration and angular rate in six degrees of freedom. The acceleration and angular rate are integrated to determine a location and orientation of the totem IMU 156. Due to integration errors, the fused pose 174 may drift over time.
In
The head unit cameras 62 routinely capture images of the totem 34 together with the images of the real-world objects such as the real-world object 14. The images that are captured by the head unit cameras 62 are entered into the SLAM system 48. The SLAM system 48, in addition to determining the locations of the real-world objects such as the real-world object 14, also determines the location of the totem 34. As such, the SLAM system 48 establishes a relationship 180 of the totem 34 relative to the head unit 18. The SLAM system 48 also relies on data from the EM receiver 150 for establishing the relationship 180.
The SLAM system 48 also establishes a relationship 182 of the head unit relative to the world frame 172. As mentioned earlier, the fusion routine 60 receives an input from the SLAM system 48. The fusion routine used the relationship 182 of the head unit to the world frame, i.e. the head pose, as part of the calculations of the fused model of the pose of the totem 34.
The relative pose of the totem 34 to the head unit 18 is established by solving the EM dipole model from the measurement by the EM Receiver 150. The two relationships 180 and 182 thus establish a pose of the totem 34 within the world frame 172. The relationship of the totem 34 and the world frame 172 is stored as an unfused pose 184 within the world frame 172.
The comparator 164 executes synchronously together with the unfused pose determination modeler 162. The comparator 164 compares the fused pose 174 with the unfused location 184. The comparator 164 then enters a difference between the fused pose 174 and the unfused pose 184 into the drift declarer 166. The drift declarer 166 declares a drift only if the difference between the fused pose 174 and unfused pose 184 is more than a predetermined maximum distance 188 that is stored within the vision algorithms 38. The predetermined maximum distance 188 is typically less than 100 mm, and is preferably on the order of 30 mm, 20 mm or more preferably 10 mm and are determined or tuned through data analysis of the sensor fusion system. The drift declarer 166 does not declare a drift if the difference between the fused pose 174 and unfused pose 184 is less than the predetermined maximum distance 188.
When the drift declarer 166 declares a drift, the drift declarer 166 enters the pose reset routine 168. The pose reset routine 168 uses the unfused pose 184 to reset the fused pose 174 in the fusion routine 160, so the drifting is stopped and fusion routine 160 re-starts a pose tracking with the drifting being eliminated.
The exemplary computer system 900 includes a processor 902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 904 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), and a static memory 906 (e.g., flash memory, static random access memory (SRAM), etc.), which communicate with each other via a bus 908.
The computer system 900 may further include a disk drive unit 916, and a network interface device 920.
The disk drive unit 916 includes a machine-readable medium 922 on which is stored one or more sets of instructions 924 (e.g., software) embodying any one or more of the methodologies or functions described herein. The software may also reside, completely or at least partially, within the main memory 904 and/or within the processor 902 during execution thereof by the computer system 900, the main memory 904 and the processor 902 also constituting machine-readable media.
The software may further be transmitted or received over a network 928 via the network interface device 920.
The computer system 900 includes a laser driver chip 950 that is used to drive projectors to generate laser light. The laser driver chip 950 includes its own data store 960 and its own processor 962.
While the machine-readable medium 922 is shown in an exemplary embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals.
While certain exemplary embodiments have been described and shown in the accompanying drawings, it is to be understood that such embodiments are merely illustrative and not restrictive of the current invention, and that this invention is not restricted to the specific constructions and arrangements shown and described since modifications may occur to those ordinarily skilled in the art.
This application is a continuation of U.S. patent application Ser. No. 16/523,779, filed on Jul. 26, 2019, which claims priority from U.S. Provisional Patent Application No. 62/714,609, filed on Aug. 3, 2018 and U.S. Provisional Patent Application No. 62/818,032, filed on Mar. 13, 2019, all of which are incorporated herein by reference in their entirety.
Number | Name | Date | Kind |
---|---|---|---|
4344092 | Miller | Aug 1982 | A |
4652930 | Crawford | Mar 1987 | A |
4810080 | Grendol et al. | Mar 1989 | A |
4997268 | Dauvergne | Mar 1991 | A |
5007727 | Kahaney et al. | Apr 1991 | A |
5074295 | Willis | Dec 1991 | A |
5240220 | Elberbaum | Aug 1993 | A |
5410763 | Bolle | May 1995 | A |
5455625 | Englander | Oct 1995 | A |
5495286 | Adair | Feb 1996 | A |
5497463 | Stein et al. | Mar 1996 | A |
5682255 | Friesem et al. | Oct 1997 | A |
5854872 | Tai | Dec 1998 | A |
5864365 | Sramek et al. | Jan 1999 | A |
6012811 | Chao et al. | Jan 2000 | A |
6016160 | Coombs et al. | Jan 2000 | A |
6076927 | Owens | Jun 2000 | A |
6117923 | Amagai et al. | Sep 2000 | A |
6124977 | Takahashi | Sep 2000 | A |
6191809 | Hori et al. | Feb 2001 | B1 |
6375369 | Schneider et al. | Apr 2002 | B1 |
6538655 | Kubota | Mar 2003 | B1 |
6541736 | Huang et al. | Apr 2003 | B1 |
6757068 | Foxlin | Jun 2004 | B2 |
7431453 | Hogan | Oct 2008 | B2 |
7573640 | Nivon et al. | Aug 2009 | B2 |
7724980 | Shenzhi | May 2010 | B1 |
7751662 | Kleemann | Jul 2010 | B2 |
7758185 | Lewis | Jul 2010 | B2 |
8120851 | Iwasa | Feb 2012 | B2 |
8246408 | Elliot | Aug 2012 | B2 |
8353594 | Lewis | Jan 2013 | B2 |
8508676 | Silverstein et al. | Aug 2013 | B2 |
8547638 | Levola | Oct 2013 | B2 |
8619365 | Harris et al. | Dec 2013 | B2 |
8696113 | Lewis | Apr 2014 | B2 |
8733927 | Lewis | May 2014 | B1 |
8736636 | Kang | May 2014 | B2 |
8759929 | Shiozawa et al. | Jun 2014 | B2 |
8793770 | Lim | Jul 2014 | B2 |
8823855 | Hwang | Sep 2014 | B2 |
8847988 | Geisner et al. | Sep 2014 | B2 |
8874673 | Kim | Oct 2014 | B2 |
9010929 | Lewis | Apr 2015 | B2 |
9086537 | Iwasa et al. | Jul 2015 | B2 |
9095437 | Boyden et al. | Aug 2015 | B2 |
9239473 | Lewis | Jan 2016 | B2 |
9244293 | Lewis | Jan 2016 | B2 |
9383823 | Geisner et al. | Jul 2016 | B2 |
9581820 | Robbins | Feb 2017 | B2 |
9658473 | Lewis | May 2017 | B2 |
9671566 | Abovitz et al. | Jun 2017 | B2 |
9671615 | Vallius et al. | Jun 2017 | B1 |
9874664 | Stevens et al. | Jan 2018 | B2 |
9955862 | Freeman et al. | May 2018 | B2 |
9996797 | Holz et al. | Jun 2018 | B1 |
10018844 | Levola et al. | Jul 2018 | B2 |
10082865 | Raynal et al. | Sep 2018 | B1 |
10151937 | Lewis | Dec 2018 | B2 |
10185147 | Lewis | Jan 2019 | B2 |
10218679 | Jawahar | Feb 2019 | B1 |
10241545 | Richards et al. | Mar 2019 | B1 |
10317680 | Richards et al. | Jun 2019 | B1 |
10516853 | Gibson et al. | Dec 2019 | B1 |
10551879 | Richards et al. | Feb 2020 | B1 |
10578870 | Kimmel | Mar 2020 | B2 |
10825424 | Zhang | Nov 2020 | B2 |
20010010598 | Aritake et al. | Aug 2001 | A1 |
20020063913 | Nakamura et al. | May 2002 | A1 |
20020071050 | Homberg | Jun 2002 | A1 |
20020122648 | Mule′ et al. | Sep 2002 | A1 |
20020140848 | Cooper et al. | Oct 2002 | A1 |
20030048456 | Hill | Mar 2003 | A1 |
20030067685 | Niv | Apr 2003 | A1 |
20030077458 | Korenaga et al. | Apr 2003 | A1 |
20030219992 | Schaper | Nov 2003 | A1 |
20040001533 | Tran et al. | Jan 2004 | A1 |
20040021600 | Wittenberg | Feb 2004 | A1 |
20040042377 | Nikoloai et al. | Mar 2004 | A1 |
20040174496 | Ji et al. | Sep 2004 | A1 |
20040186902 | Stewart | Sep 2004 | A1 |
20040201857 | Foxlin | Oct 2004 | A1 |
20040240072 | Schindler et al. | Dec 2004 | A1 |
20040246391 | Travis | Dec 2004 | A1 |
20050001977 | Zelman | Jan 2005 | A1 |
20050157159 | Komiya et al. | Jul 2005 | A1 |
20050273792 | Inohara et al. | Dec 2005 | A1 |
20060013435 | Rhoads | Jan 2006 | A1 |
20060015821 | Jacques Parker et al. | Jan 2006 | A1 |
20060038880 | Starkweather et al. | Feb 2006 | A1 |
20060050224 | Smith | Mar 2006 | A1 |
20060126181 | Levola | Jun 2006 | A1 |
20060132914 | Weiss et al. | Jun 2006 | A1 |
20060221448 | Nivon et al. | Oct 2006 | A1 |
20060228073 | Mukawa et al. | Oct 2006 | A1 |
20060250322 | Hall et al. | Nov 2006 | A1 |
20060268220 | Hogan | Nov 2006 | A1 |
20070058248 | Nguyen et al. | Mar 2007 | A1 |
20070159673 | Freeman et al. | Jul 2007 | A1 |
20070188837 | Shimizu et al. | Aug 2007 | A1 |
20070204672 | Huang et al. | Sep 2007 | A1 |
20070213952 | Cirelli | Sep 2007 | A1 |
20070283247 | Brenneman et al. | Dec 2007 | A1 |
20080002259 | Ishizawa et al. | Jan 2008 | A1 |
20080002260 | Arrouy et al. | Jan 2008 | A1 |
20080043334 | Itzkovitch et al. | Feb 2008 | A1 |
20080063802 | Maula et al. | Mar 2008 | A1 |
20080068557 | Menduni et al. | Mar 2008 | A1 |
20080146942 | Dala-Krishna | Jun 2008 | A1 |
20080205838 | Crippa et al. | Aug 2008 | A1 |
20080316768 | Travis | Dec 2008 | A1 |
20090153797 | Allon et al. | Jun 2009 | A1 |
20090224416 | Laakkonen et al. | Sep 2009 | A1 |
20090245730 | Kleemann | Oct 2009 | A1 |
20090310633 | Ikegami | Dec 2009 | A1 |
20100019962 | Fujita | Jan 2010 | A1 |
20100056274 | Uusitalo et al. | Mar 2010 | A1 |
20100063854 | Purvis et al. | Mar 2010 | A1 |
20100079841 | Levola | Apr 2010 | A1 |
20100232016 | Landa et al. | Sep 2010 | A1 |
20100232031 | Batchko et al. | Sep 2010 | A1 |
20100244168 | Shiozawa et al. | Sep 2010 | A1 |
20100296163 | Sarikko | Nov 2010 | A1 |
20110050655 | Mukawa | Mar 2011 | A1 |
20110122240 | Becker | May 2011 | A1 |
20110145617 | Thomson et al. | Jun 2011 | A1 |
20110170801 | Lu et al. | Jul 2011 | A1 |
20110218733 | Hamza et al. | Sep 2011 | A1 |
20110286735 | Temblay | Nov 2011 | A1 |
20110291969 | Rashid et al. | Dec 2011 | A1 |
20120050535 | Densham et al. | Mar 2012 | A1 |
20120075501 | Oyagi et al. | Mar 2012 | A1 |
20120081392 | Arthur | Apr 2012 | A1 |
20120113235 | Shintani | May 2012 | A1 |
20120154557 | Perez et al. | Jun 2012 | A1 |
20120218301 | Miller | Aug 2012 | A1 |
20120246506 | Knight | Sep 2012 | A1 |
20120249416 | Maciocci et al. | Oct 2012 | A1 |
20120249741 | Maciocci et al. | Oct 2012 | A1 |
20120307075 | Margalitq | Dec 2012 | A1 |
20120314959 | White et al. | Dec 2012 | A1 |
20120320460 | Levola | Dec 2012 | A1 |
20120326948 | Crocco et al. | Dec 2012 | A1 |
20130050833 | Lewis et al. | Feb 2013 | A1 |
20130051730 | Travers et al. | Feb 2013 | A1 |
20130077170 | Ukuda | Mar 2013 | A1 |
20130094148 | Sloane | Apr 2013 | A1 |
20130169923 | Schnoll et al. | Jul 2013 | A1 |
20130278633 | Ahn et al. | Oct 2013 | A1 |
20130314789 | Saarikko et al. | Nov 2013 | A1 |
20130336138 | Venkatraman et al. | Dec 2013 | A1 |
20130342564 | Kinnebrew et al. | Dec 2013 | A1 |
20130342570 | Kinnebrew et al. | Dec 2013 | A1 |
20130342571 | Kinnebrew et al. | Dec 2013 | A1 |
20140016821 | Arth et al. | Jan 2014 | A1 |
20140022819 | Oh et al. | Jan 2014 | A1 |
20140078023 | Ikeda et al. | Mar 2014 | A1 |
20140119598 | Ramachandran et al. | May 2014 | A1 |
20140126769 | Reitmayr et al. | May 2014 | A1 |
20140140653 | Brown et al. | May 2014 | A1 |
20140149573 | Tofighbakhsh et al. | May 2014 | A1 |
20140168260 | O'Brien et al. | Jun 2014 | A1 |
20140267419 | Ballard et al. | Sep 2014 | A1 |
20140274391 | Stafford | Sep 2014 | A1 |
20140359589 | Kodsky et al. | Dec 2014 | A1 |
20140375680 | Ackerman et al. | Dec 2014 | A1 |
20150005785 | Olson | Jan 2015 | A1 |
20150009099 | Queen | Jan 2015 | A1 |
20150077312 | Wang | Mar 2015 | A1 |
20150123966 | Newman | May 2015 | A1 |
20150130790 | Vazquez, II et al. | May 2015 | A1 |
20150134995 | Park et al. | May 2015 | A1 |
20150138248 | Schrader | May 2015 | A1 |
20150155939 | Oshima et al. | Jun 2015 | A1 |
20150205126 | Schowengerdt | Jul 2015 | A1 |
20150235431 | Schowengerdt | Aug 2015 | A1 |
20150253651 | Russell et al. | Sep 2015 | A1 |
20150256484 | Cameron | Sep 2015 | A1 |
20150269784 | Miyawaki et al. | Sep 2015 | A1 |
20150294483 | Wells et al. | Oct 2015 | A1 |
20150301955 | Yakovenko et al. | Oct 2015 | A1 |
20150338915 | Publicover et al. | Nov 2015 | A1 |
20150355481 | Hilkes et al. | Dec 2015 | A1 |
20160004102 | Nisper et al. | Jan 2016 | A1 |
20160027215 | Burns et al. | Jan 2016 | A1 |
20160077338 | Robbins et al. | Mar 2016 | A1 |
20160085300 | Robbins et al. | Mar 2016 | A1 |
20160091720 | Stafford et al. | Mar 2016 | A1 |
20160093099 | Bridges | Mar 2016 | A1 |
20160123745 | Cotier et al. | May 2016 | A1 |
20160155273 | Lyren et al. | Jun 2016 | A1 |
20160180596 | Gonzalez del Rosario | Jun 2016 | A1 |
20160202496 | Billetz et al. | Jul 2016 | A1 |
20160217624 | Finn et al. | Jul 2016 | A1 |
20160266412 | Yoshida | Sep 2016 | A1 |
20160267708 | Nistico et al. | Sep 2016 | A1 |
20160274733 | Hasegawa et al. | Sep 2016 | A1 |
20160300388 | Stafford et al. | Oct 2016 | A1 |
20160321551 | Priness et al. | Nov 2016 | A1 |
20160327798 | Xiao et al. | Nov 2016 | A1 |
20160334279 | Mittleman et al. | Nov 2016 | A1 |
20160357255 | Lindh et al. | Dec 2016 | A1 |
20160370404 | Quadrat et al. | Dec 2016 | A1 |
20160370510 | Thomas | Dec 2016 | A1 |
20170038607 | Camara | Feb 2017 | A1 |
20170061696 | Li et al. | Mar 2017 | A1 |
20170100664 | Osterhout et al. | Apr 2017 | A1 |
20170115487 | Travis | Apr 2017 | A1 |
20170122725 | Yeoh et al. | May 2017 | A1 |
20170123526 | Trail et al. | May 2017 | A1 |
20170127295 | Black et al. | May 2017 | A1 |
20170131569 | Aschwanden et al. | May 2017 | A1 |
20170147066 | Katz et al. | May 2017 | A1 |
20170160518 | Lanman et al. | Jun 2017 | A1 |
20170161951 | Fix et al. | Jun 2017 | A1 |
20170192239 | Nakamura et al. | Jul 2017 | A1 |
20170205903 | Miller | Jul 2017 | A1 |
20170206668 | Poulos et al. | Jul 2017 | A1 |
20170213388 | Margolis et al. | Jul 2017 | A1 |
20170232345 | Rofougaran et al. | Aug 2017 | A1 |
20170235126 | DiDomenico | Aug 2017 | A1 |
20170235142 | Wall et al. | Aug 2017 | A1 |
20170235144 | Piskunov et al. | Aug 2017 | A1 |
20170235147 | Kamakura | Aug 2017 | A1 |
20170243403 | Daniels et al. | Aug 2017 | A1 |
20170254832 | Ho et al. | Sep 2017 | A1 |
20170281054 | Stever et al. | Oct 2017 | A1 |
20170287376 | Bakar et al. | Oct 2017 | A1 |
20170293141 | Schowengerdt et al. | Oct 2017 | A1 |
20170307886 | Stenberg et al. | Oct 2017 | A1 |
20170307891 | Bucknor et al. | Oct 2017 | A1 |
20170312032 | Amanatullah et al. | Nov 2017 | A1 |
20170322426 | Tervo | Nov 2017 | A1 |
20170329137 | Tervo | Nov 2017 | A1 |
20170332098 | Rusanovskyy et al. | Nov 2017 | A1 |
20170357332 | Balan | Dec 2017 | A1 |
20180014266 | Chen | Jan 2018 | A1 |
20180059305 | Popovich et al. | Mar 2018 | A1 |
20180067779 | Pillalamarri et al. | Mar 2018 | A1 |
20180070855 | Eichler | Mar 2018 | A1 |
20180082480 | White et al. | Mar 2018 | A1 |
20180088185 | Woods et al. | Mar 2018 | A1 |
20180102981 | Kurtzman et al. | Apr 2018 | A1 |
20180108179 | Tomlin et al. | Apr 2018 | A1 |
20180114298 | Malaika et al. | Apr 2018 | A1 |
20180136466 | Ko | May 2018 | A1 |
20180189568 | Powderly et al. | Jul 2018 | A1 |
20180190017 | Mendez et al. | Jul 2018 | A1 |
20180191990 | Motoyama et al. | Jul 2018 | A1 |
20180250589 | Cossairt et al. | Sep 2018 | A1 |
20190005069 | Filgueiras de Araujo et al. | Jan 2019 | A1 |
20190011691 | Peyman | Jan 2019 | A1 |
20190056591 | Tervo et al. | Feb 2019 | A1 |
20190101758 | Zhu et al. | Apr 2019 | A1 |
20190167095 | Krueger | Jun 2019 | A1 |
20190172216 | Ninan et al. | Jun 2019 | A1 |
20190178654 | Hare | Jun 2019 | A1 |
20190196690 | Chong et al. | Jun 2019 | A1 |
20190243123 | Bohn | Aug 2019 | A1 |
20190318540 | Piemonte et al. | Oct 2019 | A1 |
20190321728 | Imai et al. | Oct 2019 | A1 |
20190347853 | Chen et al. | Nov 2019 | A1 |
20200117267 | Gibson et al. | Apr 2020 | A1 |
20200117270 | Gibson et al. | Apr 2020 | A1 |
20200309944 | Thoresen et al. | Oct 2020 | A1 |
20200356161 | Wagner | Nov 2020 | A1 |
20200409528 | Lee | Dec 2020 | A1 |
20210033871 | Jacoby et al. | Feb 2021 | A1 |
20210041951 | Gibson et al. | Feb 2021 | A1 |
20210142582 | Jones et al. | May 2021 | A1 |
20210158627 | Cossairt et al. | May 2021 | A1 |
Number | Date | Country |
---|---|---|
0535402 | Apr 1993 | EP |
1215522 | Jun 2002 | EP |
1938141 | Jul 2008 | EP |
1943556 | Jul 2008 | EP |
3164776 | May 2017 | EP |
3236211 | Oct 2017 | EP |
2723240 | Aug 2018 | EP |
2003-029198 | Jan 2003 | JP |
2007-012530 | Jan 2007 | JP |
2009-244869 | Oct 2009 | JP |
2012-015774 | Jan 2012 | JP |
2016-85463 | May 2016 | JP |
6232763 | Nov 2017 | JP |
201803289 | Jan 2018 | TW |
2002071315 | Sep 2002 | WO |
2006132614 | Dec 2006 | WO |
2007085682 | Aug 2007 | WO |
2007102144 | Sep 2007 | WO |
2008148927 | Dec 2008 | WO |
2009101238 | Aug 2009 | WO |
2013049012 | Apr 2013 | WO |
2015143641 | Oct 2015 | WO |
2016054092 | Apr 2016 | WO |
2017004695 | Jan 2017 | WO |
2017120475 | Jul 2017 | WO |
2018044537 | Mar 2018 | WO |
2018087408 | May 2018 | WO |
2018166921 | Sep 2018 | WO |
2019148154 | Aug 2019 | WO |
2020010226 | Jan 2020 | WO |
Entry |
---|
Communication Pursuant to Article 94(3) EPC dated Sep. 4, 2019, European Patent Application No. 10793707.0, (4 pages). |
Examination Report dated Jun. 19, 2020, European Patent Application No. 20154750.2, (10 pages). |
Extended European Search Report dated May 20, 2020, European Patent Application No. 20154070.5, (7 pages). |
Extended European Search Report dated Jun. 12, 2017, European Patent Application No. 16207441.3, (8 pages). |
Final Office Action dated Aug. 10, 2020, U.S. Appl. No. 16/225,961, (13 pages). |
Final Office Action dated Dec. 4, 2019, U.S. Appl. No. 15/564,517, (15 pages). |
Final Office Action dated Feb. 19, 2020, U.S. Appl. No. 15/552,897, (17 pages). |
International Search Report and Written Opinion dated Mar. 12, 2020, International PCT Patent Application No. PCT/US19/67919, (14 pages). |
International Search Report and Written Opinion dated Aug. 15, 2019, International PCT Patent Application No. PCT/US19/33987, (20 pages). |
International Search Report and Written Opinion dated Jun. 15, 2020, International PCT Patent Application No. PCT/US2020/017023, (13 pages). |
International Search Report and Written Opinion dated Oct. 16, 2019, International PCT Patent Application No. PCT/US19/43097, (10 pages). |
International Search Report and Written Opinion dated Oct. 16, 2019, International PCT Patent Application No. PCT/US19/36275, (10 pages). |
International Search Report and Written Opinion dated Oct. 16, 2019, International PCT Patent Application No. PCT/US19/43099, (9 pages). |
International Search Report and Written Opinion dated Jun. 17, 2016, International PCT Patent Application No. PCT/FI2016/050172, (9 pages). |
International Search Report and Written Opinion dated Oct. 22, 2019, International PCT Patent Application No. PCT/US19/43751, (9 pages). |
International Search Report and Written Opinion dated Dec. 23, 2019, International PCT Patent Application No. PCT/US19/44953, (11 pages). |
International Search Report and Written Opinion dated May 23, 2019, International PCT Patent Application No. PCT/US18/66514, (17 pages). |
International Search Report and Written Opinion dated Sep. 26, 2019, International PCT Patent Application No. PCT/US19/40544, (12 pages). |
International Search Report and Written Opinion dated Aug. 27, 2019, International PCT Application No. PCT/US2019/035245, (8 pages). |
International Search Report and Written Opinion dated Dec. 27, 2019, International Application No. PCT/US19/47746, (16 pages). |
International Search Report and Written Opinion dated Sep. 30, 2019, International Patent Application No. PCT/US19/40324, (7 pages). |
International Search Report and Written Opinion dated Jun. 5, 2020, International Patent Application No. PCT/US20/19871, (9 pages). |
International Search Report and Written Opinion dated Aug. 8, 2019, International PCT Patent Application No. PCT/US2019/034763, (8 pages). |
International Search Report and Written Opinion dated Oct. 8, 2019, International PCT Patent Application No. PCT/US19/41151, (7 pages). |
International Search Report and Written Opinion dated Jan. 9, 2020, International Application No. PCT/US19/55185, (10 pages). |
International Search Report and Written Opinion dated Feb. 28, 2019, International Patent Application No. PCT/US18/64686, (8 pages). |
International Search Report and Written Opinion dated Feb. 7, 2020, International PCT Patent Application No. PCT/US2019/061265, (11 pages). |
International Search Report and Written Opinion dated Jun. 11, 2019, International PCT Application No. PCT/US19/22620, (7 pages). |
Invitation to Pay Additional Fees dated Aug. 15, 2019, International PCT Patent Application No. PCT/US19/36275, (2 pages). |
Invitation to Pay Additional Fees dated Oct. 22, 2019, International PCT Patent Application No. PCT/US 19/47746, (2 pages). |
Invitation to Pay Additional Fees dated Apr. 3, 2020, International Patent Application No. PCT/US20/17023, (2 pages). |
Invitation to Pay Additional Fees dated Oct. 17, 2019, International PCT Patent Application No. PCT/US19/44953, (2 pages). |
Non Final Office Action dated Aug. 21, 2019, U.S. Appl. No. 15/564,517, (14 pages). |
Non Final Office Action dated Jul. 27, 2020, U.S. Appl. No. 16/435,933, (16 pages). |
Non Final Office Action dated Jun. 17, 2020, U.S. Appl. No. 16/682,911, (22 pages). |
Non Final Office Action dated Jun. 19, 2020, U.S. Appl. No. 16/225,961, (35 pages). |
Non Final Office Action dated Nov. 19, 2019, U.S. Appl. No. 16/355,611, (31 pages). |
Non Final Office Action dated Oct. 22, 2019, U.S. Appl. No. 15/859,277, (15 pages). |
Notice of Allowance dated Mar. 25, 2020, U.S. Appl. No. 15/564,517, (11 pages). |
Summons to attend oral proceedings pursuant to Rule 115(1) EPC mailed on Jul. 15, 2019, European Patent Application No. 15162521.7, (7 pages). |
Aarik, J. et al., “Effect of crystal structure on optical properties of TiO2 films grown by atomic layer deposition”, Thin Solid Films; Publication [online). May 19, 1998 [retrieved Feb. 19, 2020], Retrieved from the Internet: <URL https://www.sciencedirect.com/science/article/pii/S0040609097001351?via%3Dihub>; DOI: 10.1016/S0040-6090(97)00135-1; see entire document, (2 pages). |
Azom, , “Silica—Silicon Dioxide (SiO2)”, AZO Materials; Publication [Online], Dec. 13, 2001 [retrieved Feb. 19, 2020], Retrieved from the Internet: <URL: https://www.azom.com/article.aspx7Article1D=1114>, (6 pages). |
Goodfellow, , “Titanium Dioxide—Titania (TiO2)”, AZO Materials; Publication [online], Jan. 11, 2002 [retrieved Feb. 19, 2020], Retrieved from the Internet: <URL: https://www.azom.com/article.aspx?Article1D=1179>, (9 pages). |
Levola, T. , “Diffractive Optics for Virtual Reality Displays”, Journal of the SID Eurodisplay May 14, 2005, XP008093627, chapters 2-3, Figures 2 and 10, pp. 467-475. |
Levola, Tapani , “Invited Paper: Novel Diffractive Optical Components for Near to Eye Displays—Nokia Research Center”, SID 2006 Digest, 2006 SID International Symposium, Society for Information Display, vol. XXXVII, May 24, 2005, chapters 1-3, figures 1 and 3, pp. 64-67. |
Memon, F. et al., “Synthesis, Characterization and Optical Constants of Silicon Oxycarbide”, EPJ Web of Conferences; Publication [online). Mar. 23, 2017 [retrieved Feb. 19, 2020).<URL: https://www.epj-conferences.org/articles/epjconf/pdf/2017/08/epjconf_nanop2017_00002.pdf> DOI: 10.1051/epjconf/201713900002, (8 pages). |
Spencer, T. et al., “Decomposition of poly(propylene carbonate) with UV sensitive iodonium 11 salts”, Polymer Degradation and Stability (online], Dec. 24, 2010 (retrieved Feb. 19, 2020]., <URL: http:/fkohl.chbe.gatech.edu/sites/default/files/linked_files/publications/2011Decomposition%20of%20poly(propylene%20carbonate)%20with%20UV%20sensitive%20iodonium%20salts,pdf>; DOI: 10, 1016/j.polymdegradstab.2010, 12.003, (17 pages). |
Weissel, et al., “Process cruise control: event-driven clock scaling for dynamic power management”, Proceedings of the 2002 international conference on Compilers, architecture, and synthesis for embedded systems. Oct. 11, 2002 (Oct. 11, 2002) Retrieved on May 16, 2020 (May 16, 2020) from <URL: https://dl.acm.org/doi/pdf/10.1145/581630.581668>, p. 238-246. |
“ARToolKit: Hardware”, https://web.archive.org/web/20051013062315/http://www.hitl.washington.edu:80/artoolkit/documentation/hardware.htm (downloaded Oct. 26, 2020), Oct. 13, 2015, (3 pages). |
International Search Report and Written Opinion dated Sep. 4, 2020, International Patent Application No. PCT/US20/31036, (13 pages). |
Invitation to Pay Additional Fees dated Sep. 24, 2020, International Patent Application No. PCT/US2020/043596, (3 pages). |
Non Final Office Action dated Sep. 1, 2020, U.S. Appl. No. 16/214,575, (40 pages). |
Notice of Allowance dated Oct. 5, 2020, U.S. Appl. No. 16/682,911, (27 pages). |
Notice of Reason of Refusal dated Sep. 11, 2020 with English translation, Japanese Patent Application No. 2019-140435, (6 pages). |
Azuma, Ronald T. , “A Survey of Augmented Reality”, Presence Teleoperators and Virtual Environments 6, 4 (Aug. 1997), 355-385; https://web.archive.org/web/20010604100006/http://www.cs.unc.edu/˜azuma/ARpresence.pdf (downloaded Oct. 26, 2020). |
Azuma, Ronald T. , “Predictive Tracking for Augmented Reality”, Department of Computer Science, Chapel Hill NC; TR95-007, Feb. 1995, 262 pages. |
Bimber, Oliver et al., “Spatial Augmented Reality: Merging Real and Virtual Worlds”, https://web.media.mit.edu/˜raskar/book/BimberRaskarAugmentedRealityBook.pdf; published by A K Peters/CRC Press (Jul. 31, 2005); eBook (3rd Edition, 2007), (393 pages). |
Jacob, Robert J. , “Eye Tracking in Advanced Interface Design”, Human-Computer Interaction Lab, Naval Research Laboratory, Washington, D.C., date unknown. 2003, pp. 1-50. |
Tanriverdi, Vildan et al., “Interacting With Eye Movements in Virtual Environments”, Department of Electrical Engineering and Computer Science, Tufts University; Proceedings of the SIGCHI conference on Human Factors in Computing Systems, Apr. 2000, pp. 1-8. |
European Search Report dated Oct. 15, 2020, European Patent Application No. 20180623.9, (10 pages). |
Extended European Search Report dated Jan. 22, 2021, European Patent Application No. 18890390.0, (11 pages). |
Extended European Search Report dated Nov. 3, 2020, European Patent Application No. 18885707.2, (7 pages). |
Extended European Search Report dated Jun. 30, 2021, European Patent Application No. 19811971.1, (9 pages). |
Extended European Search Report dated Mar. 4, 2021, European Patent Application No. 19768418.6, (9 pages). |
Extended European Search Report dated Nov. 4, 2020, European Patent Application No. 20190980.1, (14 pages). |
Final Office Action dated Jun. 15, 2021, U.S. Appl. No. 16/928,313, (42 pages). |
Final Office Action dated Mar. 1, 2021, U.S. Appl. No. 16/214,575, (29 pages). |
Final Office Action dated Mar. 19, 2021, U.S. Appl. No. 16/530,776, (25 pages). |
Final Office Action dated Nov. 24, 2020, U.S. Appl. No. 16/435,933, (44 pages). |
International Search Report and Written Opinion dated Feb. 12, 2021, International Application No. PCT/US20/60555, (25 pages). |
International Search Report and Written Opinion dated Feb. 2, 2021, International PCT Patent Application No. PCT/US20/60550, (9 pages). |
International Search Report and Written Opinion dated Dec. 3, 2020, International Patent Application No. PCT/US20/43596, (25 pages). |
Non Final Office Action dated Jan. 26, 2021, U.S. Appl. No. 16/928,313, (33 pages). |
Non Final Office Action dated Jan. 27, 2021, U.S. Appl. No. 16/225,961, (15 pages). |
Non Final Office Action dated Jul. 9, 2021, U.S. Appl. No. 16/833,093, (47 pages). |
Non Final Office Action dated Jun. 10, 2021, U.S. Appl. No. 16/938,782, (40 Pages). |
Non Final Office Action dated Mar. 3, 2021, U.S. Appl. No. 16/427,337, (41 pages). |
Non Final Office Action dated May 26, 2021, U.S. Appl. No. 16/214,575, (19 pages). |
Non Final Office Action dated Nov. 5, 2020, U.S. Appl. No. 16/530,776, (45 pages). |
“Phototourism Challenge”, CVPR 2019 Image Matching Workshop. https://image matching-workshop. github.io., (16 pages). |
Altwaijry, et al., “Learning to Detect and Match Keypoints with Deep Architectures”, Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, Sep. 2016, [retrieved on Jan. 8, 2021 (Jan. 8, 2021 )] < URL: http://www.bmva.org/bmvc/2016/papers/paper049/index.html >, en lire document, especially Abstract, pp. 1-6 and 9. |
Arandjelović, Relja et al., “Three things everyone should know to improve object retrieval”, CVPR, 2012, (8 pages). |
Battaglia, Peter W. et al., “Relational inductive biases, deep learning, and graph networks”, arXiv:1806.01261, Oct. 17, 2018, pp. 1-40. |
Berg, Alexander C et al., “Shape matching and object recognition using low distortion correspondences”, In CVPR, 2005, (8 pages). |
Bian, Jiawang et al., “GMS: Grid-based motion statistics for fast, ultra-robust feature correspondence.”, In CVPR (Conference on Computer Vision and Pattern Recognition), 2017, (10 pages). |
Brachmann, Eric et al., “Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses”, In ICCV (International Conference on Computer Vision ), arXiv:1905.04132v2 [cs.CV] Jul. 31, 2019, (17 pages). |
Butail, et al., “Putting the fish in the fish tank: Immersive VR for animal behavior experiments”, In: 2012 IEEE International Conference on Robotics and Automation. May 18, 2012 (May 18, 2012) Retrieved on Nov. 14, 2020 (Nov. 14, 2020) from <http:/lcdcl.umd.edu/papers/icra2012.pdf> entire document, (8 pages). |
Caetano, Tibério S et al., “Learning graph matching”, IEEE TPAMI, 31(6):1048-1058, 2009. |
Cech, Jan et al., “Efficient sequential correspondence selection by cosegmentation”, IEEE TPAMI, 32(9):1568-1581, Sep. 2010. |
Cuturi, Marco , “Sinkhorn distances: Lightspeed computation of optimal transport”, NIPS, 2013, (9 pages). |
Dai, Angela et al., “ScanNet: Richly-annotated 3d reconstructions of indoor scenes”, In CVPR, arXiv:1702.04405v2 [cs.CV] Apr. 11, 2017, (22 pages). |
Deng, Haowen et al., “PPFnet: Global context aware local features for robust 3d point matching”, In CVPR, arXiv:1802.02669v2 [cs.CV] Mar. 1, 2018, (12 pages). |
Detone, Daniel et al., “Deep image homography estimation”, In RSS Work-shop: Limits and Potentials of Deep Learning in Robotics, arXiv:1606.03798v1 [cs.CV] Jun. 13, 2016, (6 pages). |
Detone, Daniel et al., “Self-improving visual odometry”, arXiv:1812.03245, Dec. 8, 2018, (9 pages). |
Detone, Daniel et al., “SuperPoint: Self-supervised interest point detection and description”, In CVPR Workshop on Deep Learning for Visual SLAM, arXiv:1712.07629v4 [cs.CV] Apr. 19, 2018, (13 pages). |
Dusmanu, Mihai et al., “D2-net: A trainable CNN for joint detection and description of local features”, CVPR, arXiv:1905.03561v1 [cs.CV] May 9, 2019, (16 pages). |
Ebel, Patrick et al., “Beyond cartesian representations for local descriptors”, ICCV, arXiv:1908.05547v1 [cs.CV] Aug. 15, 2019, (11 pages). |
Fischler, Martin A et al., “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”, Communications of the ACM, 24(6): 1981, pp. 381-395. |
Gilmer, Justin et al., “Neural message passing for quantum chemistry”, In ICML, arXiv:1704.01212v2 [cs.LG] Jun. 12, 2017, (14 pages). |
Hartley, Richard et al., “Multiple View Geometry in Computer Vision”, Cambridge University Press, 2003, pp. 1-673. |
Lee, et al., “Self-Attention Graph Pooling”, Cornell University Library/Computer Science/ Machine Learning, Apr. 17, 2019 [retrieved on Jan. 8, 2021 from the Internet< URL: https://arxiv.org/abs/1904.08082 >, entire document. |
Lee, Juho et al., “Set transformer: A frame-work for attention-based permutation-invariant neural networks”, ICML, arXiv:1810.00825v3 [cs.LG] May 26, 2019, (17 pages). |
Leordeanu, Marius et al., “A spectral technique for correspondence problems using pairwise constraints”, Proceedings of (ICCV) International Conference on Computer Vision, vol. 2, pp. 1482-1489, Oct. 2005, (8 pages). |
Li, Yujia et al., “Graph matching networks for learning the similarity of graph structured objects”, ICML, arXiv:1904.12787v2 [cs.LG] May 12, 2019, (18 pages). |
Li, Zhengqi et al., “Megadepth: Learning single-view depth prediction from internet photos”, In CVPR, fromarXiv: 1804.00607v4 [cs.CV] Nov. 28, 2018, (10 pages). |
Libovicky, et al., “Input Combination Strategies for Multi-Source Transformer Decoder”, Proceedings of the Third Conference on Machine Translation (WMT). vol. 1: Research Papers, Belgium, Brussels, Oct. 31-Nov. 1, 2018; retrieved on Jan. 8, 2021 (Jan. 8, 2021 ) from < URL: https://doi.org/10.18653/v1/W18-64026 >, entire document, pp. 253-260. |
Loiola, Eliane M. et al., “A survey for the quadratic assignment problem”, European journal of operational research, 176(2): 2007, pp. 657-690. |
Lowe, David G. , “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, 60(2): 91-110, 2004, (28 pages). |
Luo, Zixin et al., “ContextDesc: Local descriptor augmentation with cross-modality context”, CVPR, arXiv:1904.04084v1 [cs.CV] Apr. 8, 2019, (14 pages). |
Molchanov, Pavlo et al., “Short-range FMCW monopulse radar for hand-gesture sensing”, 2015 IEEE Radar Conference (RadarCon) (2015), pp. 1491-1496. |
Munkres, James , “Algorithms for the assignment and transportation problems”, Journal of the Society for Industrial and Applied Mathematics, 5(1): 1957, pp. 32-38. |
Ono, Yuki et al., “LF-Net: Learning local features from images”, 32nd Conference on Neural Information Processing Systems (NIPS 2018), arXiv:1805.09662v2 [cs.CV] Nov. 22, 2018, (13 pages). |
Paszke, Adam et al., “Automatic differentiation in Pytorch”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, (4 pages). |
Peyré, Gabriel et al., “Computational Optimal Transport”, Foundations and Trends in Machine Learning, 11(5-6):355-607, 2019; arXiv:1803.00567v4 [stat.ML] Mar. 18, 2020, (209 pages). |
Qi, Charles R. et al., “Pointnet++: Deep hierarchical feature learning on point sets in a metric space.”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA., (10 pages). |
Qi, Charles R et al., “Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation”, CVPR, arXiv:1612.00593v2 [cs.CV] Apr. 10, 201, (19 pages). |
Radenović, Filip et al., “Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking”, CVPR, arXiv:1803.11285v1 [cs.CV] Mar. 29, 2018, (10 pages). |
Raguram, Rahul et al., “A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus”, Computer Vision—ECCV 2008, 10th European Conference on Computer Vision, Marseille, France, Oct. 12-18, 2008, Proceedings, Part I, (15 pages). |
Ranftl, René et al., “Deep fundamental matrix estimation”, European Conference on Computer Vision (ECCV), 2018, (17 pages). |
Revaud, Jerome et al., “R2D2: Repeatable and Reliable Detector and Descriptor”, In NeurIPS, arXiv:1906.06195v2 [cs.CV] Jun. 17, 2019, (12 pages). |
Rocco, Ignacio et al., “Neighbourhood Consensus Networks”, 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, arXiv:1810.10510v2 [cs.CV] Nov. 29, 2018, (20 pages). |
Rublee, Ethan et al., “ORB: An efficient alternative to SIFT or SURF”, Proceedings of the IEEE International Conference on Computer Vision. 2564-2571. 2011; 10.1109/ICCV.2011.612654, (9 pages). |
Sarlin, et al., “SuperGlue: Learning Feature Matching with Graph Neural Networks”, Cornell University Library/Computer Science/Computer Vision and Pattern Recognition, Nov. 26, 2019 [retrieved on Jan. 8, 2021 from the Internet< URL: https://arxiv.org/abs/1911.11763 >, entire document. |
Sattler, Torsten et al., “SCRAMSAC: Improving RANSAC's efficiency with a spatial consistency filter”, ICCV, 2009: 2090-2097., (8 pages). |
Schonberger, Johannes L. et al., “Pixelwise view selection for un-structured multi-view stereo”, Computer Vision—ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, Oct. 11-14, 2016, Proceedings, Part III, pp. 501-518, 2016. |
Schonberger, Johannes L. et al., “Structure-from-motion revisited”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4104-4113, (11 pages). |
Sheng, Liu et al., “Time-multiplexed dual-focal plane head-mounted display with a liquid lens”, Optics Letters, Optical Society of Amer i ca, US, vol. 34, No. 11, Jun. 1, 2009 (Jun. 1, 2009), XP001524475, ISSN: 0146-9592, pp. 1642-1644. |
Sinkhorn, Richard et al., “Concerning nonnegative matrices and doubly stochastic matrices.”, Pacific Journal of Mathematics, 1967, pp. 343-348. |
Thomee, Bart et al., “YFCC100m: The new data in multimedia Yesearch”, Communications of the ACM, 59(2):64-73, 2016; arXiv:1503.01817v2 [cs.MM] Apr. 25, 2016, (8 pages). |
Torresani, Lorenzo et al., “Feature correspondence via graph matching: Models and global optimization”, Computer Vision—ECCV 2008, 10th European Conference on Computer Vision, Marseille, France, Oct. 12-18, 2008, Proceedings, Part II, (15 pages). |
Tuytelaars, Tinne et al., “Wide baseline stereo matching based on local, affinely invariant regions”, BMVC, 2000, pp. 1-14. |
Ulyanov, Dmitry et al., “Instance normalization: The missing ingredient for fast stylization”, arXiv:1607.08022v3 [cs.CV] Nov. 6, 2017, (6 pages). |
Vaswani, Ashish et al., “Attention is all you need”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA; arXiv:1706.03762v5 [cs.CL] Dec. 6, 2017, (15 pages). |
Velic̆kovic̆, Petar et al., “Graph attention networks”, ICLR, arXiv:1710.10903v3 [stat.ML] Feb. 4, 2018, (12 pages). |
Mllani, Cédric , “Optimal transport: old and new”, vol. 338. Springer Science & Business Media, Jun. 2008, pp. 1-998. |
Wang, Xiaolong et al., “Non-local neural networks”, CVPR, arXiv:1711.07971v3 [cs.CV] Apr. 13, 2018, (10 pages). |
Wang, Yue et al., “Deep Closest Point: Learning representations for point cloud registration”, ICCV, arXiv:1905.03304v1 [cs.CV] May 8, 2019, (10 pages). |
Wang, Yue et al., “Dynamic Graph CNN for learning on point clouds”, ACM Transactions on Graphics, arXiv:1801.07829v2 [cs.CV] Jun. 11, 2019, (13 pages). |
Yi, Kwang M. et al., “Learning to find good correspondences”, CVPR, arXiv:1711.05971v2 [cs.CV] May 21, 2018, (13 pages). |
Yi, Kwang Moo et al., “Lift: Learned invariant feature transform”, ECCV, arXiv:1603.09114v2 [cs.CV] Jul. 29, 2016, (16 pages). |
Zaheer, Manzil et al., “Deep Sets”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA arXiv:1703.06114v3 [cs.LG] Apr. 14, 2018, (29 pages). |
Zhang, Jiahui et al., “Learning two-view correspondences and geometry using order-aware network”, ICCV; aarXiv:1908.04964v1 [cs.CV] Aug. 14, 2019, (11 pages). |
Zhang, Li et al., “Dual graph convolutional net-work for semantic segmentation”, BMVC, 2019; arXiv:1909.06121v3 [cs.CV] Aug. 26, 2020, (18 pages). |
Communication Pursuant to Rule 164(1) EPC dated Jul. 27, 2021, European Patent Application No. 19833664.6, (11 pages). |
Extended European Search Report dated Jul. 16, 2021, European Patent Application No. 19810142.0, (14 pages). |
Extended European Search Report dated Jul. 30, 2021, European Patent Application No. 19839970.1, (7 pages). |
Final Office Action dated Sep. 17, 2021, U.S. Appl. No. 16/938,782, (44 pages). |
Non Final Office Action dated Aug. 4, 2021, U.S. Appl. No. 16/864,721, (51 pages). |
Non Final Office Action dated Jun. 29, 2021, U.S. Appl. No. 16/698,588, (58 pages). |
Non Final Office Action dated Sep. 20, 2021, U.S. Appl. No. 17/105,848, (56 pages). |
Giuseppe, Donato, et al., “Stereoscopic helmet mounted system for real time 3D environment reconstruction and indoor ego—motion estimation”, Proc. SPIE 6955, Head- and Helmet-Mounted Displays XIII: Design and Applications, 69550P. |
Extended European Search Report dated Sep. 28, 2021, European Patent Application No. 19845418.3, (13 pages). |
Non Final Office Action dated Sep. 29, 2021, U.S. Appl. No. 16/748,193, (62 pages). |
Number | Date | Country | |
---|---|---|---|
20200387241 A1 | Dec 2020 | US |
Number | Date | Country | |
---|---|---|---|
62714609 | Aug 2018 | US | |
62818032 | Mar 2019 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 16523779 | Jul 2019 | US |
Child | 17002663 | US |