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. 09, 2024

Filed:

May. 13, 2022
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Ben Eckart, Oakland, CA (US);

Christopher Choy, Los Angeles, CA (US);

Chao Liu, Pittsburgh, PA (US);

Yurong You, Ithaca, NY (US);

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 17/10 (2006.01); G06T 19/20 (2011.01); G06N 3/084 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06T 17/10 (2013.01); G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06T 19/20 (2013.01); G06T 2219/2016 (2013.01);
Abstract

In various embodiments, an unsupervised training application executes a neural network on a first point cloud to generate keys and values. The unsupervised training application generates output vectors based on a first query set, the keys, and the values and then computes spatial features based on the output vectors. The unsupervised training application computes quantized context features based on the output vectors and a first set of codes representing a first set of 3D geometry blocks. The unsupervised training application modifies the first neural network based on a likelihood of reconstructing the first point cloud, the quantized context features, and the spatial features to generate an updated neural network. A trained machine learning model includes the updated neural network, a second query set, and a second set of codes representing a second set of 3D geometry blocks and maps a point cloud to a representation of 3D geometry instances.


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