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:
Feb. 10, 2026

Filed:

Oct. 30, 2023
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Yang Fu, San Diego, CA (US);

Sifei Liu, San Diego, CA (US);

Jan Kautz, Lexington, MA (US);

Xueting Li, Santa Clara, CA (US);

Shalini De Mello, San Francisco, CA (US);

Amey Kulkarni, San Jose, CA (US);

Milind Naphade, Cupertino, CA (US);

Assignee:

NVIDIA CORPORATION, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 17/20 (2006.01); G06T 7/50 (2017.01); G06T 17/00 (2006.01);
U.S. Cl.
CPC ...
G06T 17/20 (2013.01); G06T 7/50 (2017.01); G06T 17/00 (2013.01);
Abstract

In various embodiments, a training application trains a machine learning model to generate three-dimensional (3D) representations of two-dimensional images. The training application maps a depth image and a viewpoint to signed distance function (SDF) values associated with 3D query points. The training application maps a red, blue, and green (RGB) image to radiance values associated with the 3DI query points. The training application computes a red, blue, green, and depth (RGBD) reconstruction loss based on at least the SDF values and the radiance values. The training application modifies at least one of a pre-trained geometry encoder, a pre-trained geometry decoder, an untrained texture encoder, or an untrained texture decoder based on the RGBD reconstruction loss to generate a trained machine learning model that generates 3D representations of RGBD images.


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