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:
Dec. 12, 2023

Filed:

Dec. 04, 2020
Applicant:

Beijing Baidu Netcom Science and Technology Co., Ltd., Beijing, CN;

Inventors:

Xibin Song, Beijing, CN;

Dingfu Zhou, Beijing, CN;

Jin Fang, Beijing, CN;

Liangjun Zhang, Beijing, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/50 (2017.01); G06N 20/00 (2019.01); G06F 18/214 (2023.01); G06V 10/42 (2022.01); G06T 3/40 (2006.01);
U.S. Cl.
CPC ...
G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06T 3/40 (2013.01); G06T 7/50 (2017.01); G06V 10/42 (2022.01); G06T 2207/10028 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

The present application provides a model training method and apparatus, and a prediction method and apparatus, and it relates to fields of artificial intelligence, deep learning, image processing, and autonomous driving. The model training method includes: inputting a first sample image of sample images into a depth information prediction model, and acquiring depth information of the first sample image; acquiring inter-image posture information based on a second sample image of the sample images and the first sample image; acquiring a projection image corresponding to the first sample image, at least according to the inter-image posture information and the depth information; and acquiring a loss function by determining a function for calculating a similarity between the second sample image and the projection image, and training the depth information prediction model using the loss function.


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