The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 20, 2026

Filed:

Aug. 03, 2023
Applicants:

Toyota Research Institute, Inc., Los Altos, CA (US);

Massachusetts Institute of Technology, Cambridge, MA (US);

Inventors:

Vitor Guizilini, Santa Clara, CA (US);

Rares A. Ambrus, San Francisco, CA (US);

Jiading Fang, Chicago, IL (US);

Sergey Zakharov, San Francisco, CA (US);

Vincent Sitzmann, Cambridge, MA (US);

Igor Vasiljevic, Pacifica, CA (US);

Adrien Gaidon, San Jose, CA (US);

Assignees:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 15/08 (2011.01); G06T 3/18 (2024.01); G06T 15/20 (2011.01); G06V 10/25 (2022.01); G06V 10/774 (2022.01); G06V 20/40 (2022.01); G06V 20/56 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06T 15/08 (2013.01); G06T 3/18 (2024.01); G06T 15/20 (2013.01); G06V 10/25 (2022.01); G06V 10/7747 (2022.01); G06V 20/41 (2022.01); G06V 20/56 (2022.01); G06V 20/64 (2022.01);
Abstract

An example method includes generating embeddings of image data that includes multiple images, where each image has a different viewpoints of a scene, generating a latent space and a decoder, wherein the decoder receives embeddings as input to generate an output viewpoint, for each viewpoint in the image data, determining a volumetric rendering view synthesis loss and a multi-view photometric loss, and applying an optimization algorithm to the latent space and the decoder over a number of epochs until the volumetric rendering view synthesis loss is within a volumetric threshold and the multi-view photometric loss is within a multi-view threshold.


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