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：US20180350105A1

公开(公告)日：2018-12-06

申请号：US15994563

申请日：2018-05-31

Applicant: Google LLC

Inventor： Jonathan James TAYLOR ,  Vladimir TANKOVICH ,  Danhang TANG ,  Cem KESKIN ,  Adarsh Prakash Murthy KOWDLE ,  Philip L. DAVIDSON ,  Shahram IZADI ,  David KIM

IPC: G06T7/73 ,  G06K9/00

CPC classification number: G06T7/75 ,  G06K9/00382 ,  G06T7/149 ,  G06T7/162 ,  G06T7/194 ,  G06T7/251 ,  G06T2207/10016 ,  G06T2207/10028 ,  G06T2207/20076 ,  G06T2207/20081

Abstract: An electronic device estimates a pose of a hand by volumetrically deforming a signed distance field using a skinned tetrahedral mesh to locate a local minimum of an energy function, wherein the local minimum corresponds to the hand pose. The electronic device identifies a pose of the hand by fitting an implicit surface model of a hand to the pixels of a depth image that correspond to the hand. The electronic device uses a skinned tetrahedral mesh to warp space from a base pose to a deformed pose to define an articulated signed distance field from which the hand tracking module derives candidate poses of the hand. The electronic device then minimizes an energy function based on the distance of each corresponding pixel to identify the candidate pose that most closely approximates the pose of the hand.

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.

公开(公告)号：US20180300588A1

公开(公告)日：2018-10-18

申请号：US15925141

申请日：2018-03-19

Applicant: Google LLC

Inventor： Sean Ryan FANELLO ,  Julien Pascal Christophe VALENTIN ,  Adarsh Prakash Murthy KOWDLE ,  Christoph RHEMANN ,  Vladimir TANKOVICH ,  Philip L. DAVIDSON ,  Shahram IZADI

IPC: G06K9/62

CPC classification number: G06K9/6256 ,  G06K9/6202 ,  G06K9/6276 ,  G06K9/6298

Abstract: Values of pixels in an image are mapped to a binary space using a first function that preserves characteristics of values of the pixels. Labels are iteratively assigned to the pixels in the image in parallel based on a second function. The label assigned to each pixel is determined based on values of a set of nearest-neighbor pixels. The first function is trained to map values of pixels in a set of training images to the binary space and the second function is trained to assign labels to the pixels in the set of training images. Considering only the nearest neighbors in the inference scheme results in a computational complexity that is independent of the size of the solution space and produces sufficient approximations of the true distribution when the solution for each pixel is most likely found in a small subset of the set of potential solutions.

公开(公告)号：US20200160109A1

公开(公告)日：2020-05-21

申请号：US16749626

申请日：2020-01-22

Applicant: Google LLC

Inventor： Sean Ryan FANELLO ,  Julien Pascal Christophe VALENTIN ,  Adarsh Prakash Murthy KOWDLE ,  Christoph RHEMANN ,  Vladimir TANKOVICH ,  Philip L. DAVIDSON ,  Shahram IZADI

IPC: G06K9/62

Abstract: Values of pixels in an image are mapped to a binary space using a first function that preserves characteristics of values of the pixels. Labels are iteratively assigned to the pixels in the image in parallel based on a second function. The label assigned to each pixel is determined based on values of a set of nearest-neighbor pixels. The first function is trained to map values of pixels in a set of training images to the binary space and the second function is trained to assign labels to the pixels in the set of training images. Considering only the nearest neighbors in the inference scheme results in a computational complexity that is independent of the size of the solution space and produces sufficient approximations of the true distribution when the solution for each pixel is most likely found in a small subset of the set of potential solutions.

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.