公开(公告)号：US20210004979A1

公开(公告)日：2021-01-07

申请号：US16767401

申请日：2019-10-04

Applicant: Google LLC

Inventor： Jullien VALENTIN ,  Onur G. GULERYUZ ,  Mira LEUNG ,  Maksym DZITSIUK ,  Jose PASCOAL ,  Mirko SCHMIDT ,  Christoph RHEMANN ,  Neal WADHWA ,  Eric TURNER ,  Sameh KHAMIS ,  Adarsh Prakash Murthy KOWDLE ,  Ambrus CSASZAR ,  João Manuel Castro AFONSO ,  Jonathan T. BARRON ,  Michael SCHOENBERG ,  Ivan DRYANOVSKI ,  Vivek VERMA ,  Vladimir TANKOVICH ,  Shahram IZADI ,  Sean Ryan Francesco FANELLO ,  Konstantine Nicholas John TSOTSOS

IPC: G06T7/579 ,  G06T19/00

Abstract: A handheld user device includes a monocular camera to capture a feed of images of a local scene and a processor to select, from the feed, a keyframe and perform, for a first image from the feed, stereo matching using the first image, the keyframe, and a relative pose based on a pose associated with the first image and a pose associated with the keyframe to generate a sparse disparity map representing disparities between the first image and the keyframe. The processor further is to determine a dense depth map from the disparity map using a bilateral solver algorithm, and process a viewfinder image generated from a second image of the feed with occlusion rendering based on the depth map to incorporate one or more virtual objects into the viewfinder image to generate an AR viewfinder image. Further, the processor is to provide the AR viewfinder image for display.

公开(公告)号：US20200099920A1

公开(公告)日：2020-03-26

申请号：US16580802

申请日：2019-09-24

Applicant: Google LLC

Inventor： Sameh KHAMIS ,  Yinda ZHANG ,  Christoph RHEMANN ,  Julien VALENTIN ,  Adarsh KOWDLE ,  Vladimir TANKOVICH ,  Michael SCHOENBERG ,  Shahram IZADI ,  Thomas FUNKHOUSER ,  Sean FANELLO

IPC: H04N13/271 ,  G06T7/90 ,  G06T7/521 ,  H04N5/33 ,  H04N13/239

Abstract: An electronic device estimates a depth map of an environment based on matching reduced-resolution stereo depth images captured by depth cameras to generate a coarse disparity (depth) map. The electronic device downsamples depth images captured by the depth cameras and matches sections of the reduced-resolution images to each other to generate a coarse depth map. The electronic device upsamples the coarse depth map to a higher resolution and refines the upsampled depth map to generate a high-resolution depth map to support location-based functionality.

公开(公告)号：US20180350088A1

公开(公告)日：2018-12-06

申请号：US15994471

申请日：2018-05-31

Applicant: Google LLC

Inventor： Mingsong DOU ,  Sean Ryan FANELLO ,  Adarsh Prakash Murthy KOWDLE ,  Christoph RHEMANN ,  Sameh KHAMIS ,  Philip L. DAVIDSON ,  Shahram IZADI ,  Vladimir Tankovich

IPC: G06T7/593 ,  G06T15/08 ,  G06T17/20

CPC classification number: G06T7/75 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/10028

Abstract: An electronic device estimates a pose of one or more subjects in an environment based on estimating a correspondence between a data volume containing a data mesh based on a current frame captured by a depth camera and a reference volume containing a plurality of fused prior data frames based on spectral embedding and performing bidirectional non-rigid matching between the reference volume and the current data frame to refine the correspondence so as to support location-based functionality. The electronic device predicts correspondences between the data volume and the reference volume based on spectral embedding. The correspondences provide constraints that accelerate the convergence between the data volume and the reference volume. By tracking changes between the current data mesh frame and the reference volume, the electronic device avoids tracking failures that can occur when relying solely on a previous data mesh frame.

公开(公告)号：US20230209036A1

公开(公告)日：2023-06-29

申请号：US18111292

申请日：2023-02-17

Applicant: GOOGLE LLC

Inventor： Sameh KHAMIS ,  Yinda ZHANG ,  Christoph RHEMANN ,  Julien VALENTIN ,  Adarsh KOWDLE ,  Vladimir TANKOVICH ,  Michael SCHOENBERG ,  Shahram IZADI ,  Thomas FUNKHOUSER ,  Sean FANELLO

IPC: H04N13/271 ,  G06T7/90 ,  G06T7/521 ,  H04N5/33 ,  H04N13/239

CPC classification number: H04N13/271 ,  G06T7/90 ,  G06T7/521 ,  H04N5/33 ,  H04N13/239 ,  G06T2207/10024 ,  G06T2207/10028

Abstract: An electronic device estimates a depth map of an environment based on matching reduced-resolution stereo depth images captured by depth cameras to generate a coarse disparity (depth) map. The electronic device downsamples depth images captured by the depth cameras and matches sections of the reduced-resolution images to each other to generate a coarse depth map. The electronic device upsamples the coarse depth map to a higher resolution and refines the upsampled depth map to generate a high-resolution depth map to support location-based functionality.

公开(公告)号：US20180350087A1

公开(公告)日：2018-12-06

申请号：US15994509

申请日：2018-05-31

Applicant: Google LLC

Inventor： Adarsh Prakash Murthy KOWDLE ,  Vladimir TANKOVICH ,  Danhang TANG ,  Cem KESKIN ,  Jonathan James Taylor ,  Philip L. DAVIDSON ,  Shahram IZADI ,  Sean Ryan FANELLO ,  Julien Pascal Christophe VALENTIN ,  Christoph RHEMANN ,  Mingsong DOU ,  Sameh KHAMIS ,  David KIM

IPC: G06T7/593 ,  G06T7/32 ,  G06T7/33 ,  H04N13/271

CPC classification number: G06T7/521 ,  G06T7/593 ,  G06T2207/10021 ,  G06T2207/10024 ,  G06T2207/10152 ,  G06T2207/20072 ,  G06T2207/20076 ,  G06T2207/20081

Abstract: An electronic device estimates a depth map of an environment based on stereo depth images captured by depth cameras having exposure times that are offset from each other in conjunction with illuminators pulsing illumination patterns into the environment. A processor of the electronic device matches small sections of the depth images from the cameras to each other and to corresponding patches of immediately preceding depth images (e.g., a spatio-temporal image patch “cube”). The processor computes a matching cost for each spatio-temporal image patch cube by converting each spatio-temporal image patch into binary codes and defining a cost function between two stereo image patches as the difference between the binary codes. The processor minimizes the matching cost to generate a disparity map, and optimizes the disparity map by rejecting outliers using a decision tree with learned pixel offsets and refining subpixels to generate a depth map of the environment.