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：US20180356883A1

公开(公告)日：2018-12-13

申请号：US16002595

申请日：2018-06-07

Applicant: Google LLC

Inventor： Julien Pascal Christophe VALENTIN ,  Jonathan James TAYLOR ,  Shahram IZADI

IPC: G06F3/01 ,  G06T7/70

Abstract: An electronic device estimates a pose of a face by fitting a generative face model mesh to a depth map based on vertices of the face model mesh that are estimated to be visible from the point of view of a depth camera. A face tracking module of the electronic device receives a depth image of a face from a depth camera and generates a depth map of the face based on the depth image. The face tracking module identifies a pose of the face by fitting a face model mesh to the pixels of a depth map that correspond to the vertices of the face model mesh that are estimated to be visible from the point of view of the depth camera.