Google Inc.

Thomas Allen Funkhouser

Shahram Izadi

Christoph Rhemann

Vladimir Tankovich

Sean Ryan Fanello

Michael John Schoenberg

Yinda Zhang

Sameh Khamis

Julien Valentin

Adarsh Kowdle

Jonathan Tilton Barron

Pratul Preeti Srinivasan

Andrea Tagliasacchi

Vittorio Carlo Ferrari

Andrew Huaming Liu

Konstantinos Rematas

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.

Active stereo depth prediction based on coarse matching

Systems and methods for view synthesis and three-dimensional reconstruction can learn an environment by utilizing a plurality of images of an environment and depth data. The use of depth data can be helpful when the quantity of images and different angles may be limited. For example, large outdoor environments can be difficult to learn due to the size, the varying image exposures, and the limited variance in view direction changes. The systems and methods can leverage a plurality of panoramic images and corresponding lidar data to accurately learn a large outdoor environment to then generate view synthesis outputs and three-dimensional reconstruction outputs. Training may include the use of an exposure correction network to address lighting exposure differences between training images.

Radiance fields for three-dimensional reconstruction and novel view synthesis in large-scale environments

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Menlo, CA, United States of America

Thomas Allen Funkhouser