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:
Jun. 10, 2025

Filed:

Dec. 01, 2022
Applicant:

Meta Platforms, Inc., Menlo Park, CA (US);

Inventors:

Changil Kim, Seattle, WA (US);

Benjamin Attal, Pittsburgh, PA (US);

Jia-Bin Huang, Fulton, MD (US);

Johannes Peter Kopf, Medina, WA (US);

Michael Zollhoefer, Pittsburgh, PA (US);

Assignee:

Meta Platforms, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 15/06 (2011.01); G06T 7/90 (2017.01); G06T 15/20 (2011.01);
U.S. Cl.
CPC ...
G06T 15/06 (2013.01); G06T 7/90 (2017.01); G06T 15/20 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

In particular embodiments, a computing system may access a set of training images for training a neural light field network for view synthesis. Using each training image, the computing system may train the neural light field network by casting, for each pixel of the training image, a ray into a three-dimensional (3D) space, the ray including integrated radiance along the ray, mapping first ray coordinates of the ray into an embedding network, transforming, using the embedding network, the first ray coordinates into second ray coordinates, applying positional encoding to second ray coordinates, generating, using the neural light field network, a predicted color value for the pixel based on positionally encoded second ray coordinates, comparing the predicted color value for the pixel with a ground-truth color value for the pixel, and updating the neural light field network and the embedding network based on comparison.


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