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:
Aug. 05, 2025

Filed:

Feb. 28, 2025
Applicant:

Hangzhou City University, Zhejiang, CN;

Inventors:

Junjie Jiang, Zhejiang, CN;

Anping Wan, Zhejiang, CN;

Xiaomin Cheng, Zhejiang, CN;

Junhao Huang, Zhejiang, CN;

Kaiyang Wang, Zhejiang, CN;

Assignee:

HANGZHOU CITY UNIVERSITY, Hangzhou, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 15/00 (2011.01); G06T 5/70 (2024.01); G06T 7/80 (2017.01); G06V 10/44 (2022.01); G06V 10/80 (2022.01); H04N 19/597 (2014.01);
U.S. Cl.
CPC ...
G06T 15/00 (2013.01); G06T 5/70 (2024.01); G06T 7/80 (2017.01); G06V 10/44 (2022.01); G06V 10/806 (2022.01); H04N 19/597 (2014.11);
Abstract

A generalizable neural radiation field reconstruction method based on multi-modal information fusion, including: Step 1, constructing photometric features and geometric features based on unstructured multi-views, and constructing a multi-modal neural encoder by performing incrementally complementary fusion on the photometric features and the geometric features; Step 2, converting the multi-modal neural encoder and raw RGB pixel bodies of the unstructured multi-views into a volume density and radiation brightness; Step 3, sampling light on the basis of the constructed multi-modal neural encoder, aggregating context features of the sampled light based on a transformer network to obtain light context features; and Step 4, decoding, using the light context features, the volume density and the radiation brightness; rendering, based on the decoded volume density and the radiation brightness, to generate a free-view RGB-D image; and guiding dense reconstruction of a low-texture scene by combining photometric supervision and sparse geometric supervision.


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