Google Inc.

Abhishek Kar

William T Freeman

Ce Liu

Aveek Ravishekhar Purohit

Adarsh Prakash Murthy Kowdle

Dominik Philemon Kaeser

Chao X Guo

Richard Tucker

Konstantine Nicholas John Tsotsos

Michael Krainin

Eric Turner

Huiwen Chang

Yunwen Zhou

David Salesin

Brian Curless

Hossam Isack

Kyle Sargent

Dmitry Medvedev

Varun Jampani

Ryan Christopher DuToit

Brain L Curless

Dominik Kaeser

William T Frreeman

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.

Depth estimation using a neural network

Disclosed is a method including receiving a depth map estimated using data based on image and data received from a movement sensor as input, generating an alignment parameter based on the depth map, adding the alignment parameter to a pre-calibration state to define a user operational calibration state, generating scale parameters and shift parameters based on features associated with the data received from the image and movement sensor, and calibrating the image and movement sensor based on the user operational calibration state, the scale parameters and the shift parameters.

Visual inertial odometry with machine learning depth

A method includes determining, based on an image having an initial viewpoint, a depth image, and determining a foreground visibility map including visibility values that are inversely proportional to a depth gradient of the depth image. The method also includes determining, based on the depth image, a background disocclusion mask indicating a likelihood that pixel of the image will be disoccluded by a viewpoint adjustment. The method additionally includes generating, based on the image, the depth image, and the background disocclusion mask, an inpainted image and an inpainted depth image. The method further includes generating, based on the depth image and the inpainted depth image, respectively, a first three-dimensional (3D) representation of the image and a second 3D representation of the inpainted image, and generating a modified image having an adjusted viewpoint by combining the first and second 3D representation based on the foreground visibility map.

Single image 3D photography with soft-layering and depth-aware inpainting

Growing community of inventors

Sunnyvale, CA, United States of America

Abhishek Kar