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:
Oct. 24, 2023

Filed:

Nov. 16, 2020
Applicants:

Ulrich Berger, Menlo Park, CA (US);

Jingwei Wang, San Jose, CA (US);

Huy Tho Ho, San Jose, CA (US);

Kjell Fredrik Larsson, San Jose, CA (US);

Inventors:

Ulrich Berger, Menlo Park, CA (US);

Jingwei Wang, San Jose, CA (US);

Huy Tho Ho, San Jose, CA (US);

Kjell Fredrik Larsson, San Jose, CA (US);

Assignee:

APPLE INC., Cupertino, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/246 (2017.01); G06T 15/08 (2011.01); G06T 7/11 (2017.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06V 30/262 (2022.01); G06F 18/231 (2023.01); G06F 18/25 (2023.01);
U.S. Cl.
CPC ...
G06T 7/248 (2017.01); G06F 18/231 (2023.01); G06F 18/251 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G06T 15/08 (2013.01); G06V 30/274 (2022.01); G06T 2207/10028 (2013.01); G06T 2207/20072 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30252 (2013.01);
Abstract

Systems and methods for moving point detection for point clouds. Some implementations may include obtaining a sequence of point clouds, wherein the sequence of point clouds includes a current point cloud, a previous point cloud that precedes the current point cloud, and a next point cloud that follows the current point cloud; inputting a voxelized representation of the current point cloud to a forward-pass recurrent neural network with a hidden state based on the previous point cloud to obtain a first voxelized activation; inputting the voxelized representation of the current point cloud to a backward-pass recurrent neural network with a hidden state based on the next point cloud to obtain a second voxelized activation; combining the first voxelized activation and the second voxelized activation to obtain a prediction of whether respective points are moving or static; and updating the current point cloud based on the prediction.


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