公开(公告)号：US20220335638A1

公开(公告)日：2022-10-20

申请号：US17596794

申请日：2021-04-19

Applicant: Google LLC

Inventor： Abhishek Kar ,  Hossam Isack ,  Adarsh Prakash Murthy Kowdle ,  Aveek Purohit ,  Dmitry Medvedev

IPC: G06T7/593 ,  G06T7/285

Abstract: According to an aspect, a method for depth estimation includes receiving image data from a sensor system, generating, by a neural network, a first depth map based on the image data, where the first depth map has a first scale, obtaining depth estimates associated with the image data, and transforming the first depth map to a second depth map using the depth estimates, where the second depth map has a second scale.

公开(公告)号：US11335023B2

公开(公告)日：2022-05-17

申请号：US15929811

申请日：2020-05-22

Applicant: Google LLC

Inventor： Sameh Khamis ,  Christian Haene ,  Hossam Isack ,  Cem Keskin ,  Sofien Bouaziz ,  Shahram Izadi

IPC: G06K9/00 ,  G06T7/73 ,  G06T3/40 ,  G06T11/00 ,  G06N3/08 ,  G06N3/04

Abstract: According to an aspect, a method for pose estimation using a convolutional neural network includes extracting features from an image, downsampling the features to a lower resolution, arranging the features into sets of features, where each set of features corresponds to a separate keypoint of a pose of a subject, updating, by at least one convolutional block, each set of features based on features of one or more neighboring keypoints using a kinematic structure, and predicting the pose of the subject using the updated sets of features.

公开(公告)号：US20210366146A1

公开(公告)日：2021-11-25

申请号：US15929811

申请日：2020-05-22

Applicant: Google LLC

Inventor： Sameh Khamis ,  Christian Haene ,  Hossam Isack ,  Cem Keskin ,  Sofien Bouaziz ,  Shahram Izadi

IPC: G06T7/73 ,  G06T3/40 ,  G06T11/00 ,  G06N3/04 ,  G06N3/08

Abstract: According to an aspect, a method for pose estimation using a convolutional neural network includes extracting features from an image, downsampling the features to a lower resolution, arranging the features into sets of features, where each set of features corresponds to a separate keypoint of a pose of a subject, updating, by at least one convolutional block, each set of features based on features of one or more neighboring keypoints using a kinematic structure, and predicting the pose of the subject using the updated sets of features.