Title:
POINT CLOUD FEATURE EXTRACTION NETWORK MODEL TRAINING METHOD, POINT CLOUD FEATURE EXTRACTION METHOD, APPARATUS, AND DRIVERLESS VEHICLE
Document Type and Number:
WIPO Patent Application WO/2024/055551
Kind Code:
A1
Abstract:
The present disclosure relates to the technical field of driverless vehicles, and provides a point cloud feature extraction network model training method, a point cloud feature extraction method, an apparatus, and a driverless vehicle. The point cloud feature extraction network model training method comprises: performing first encoding on a sample point cloud frame sequence by using a first feature extraction network model to obtain an encoded feature map of each sample point cloud frame in the sample point cloud frame sequence; according to the encoded feature maps of a plurality of adjacent sample point cloud frames, determining a predicted feature map of the next sample point cloud frame following the plurality of adjacent sample point cloud frames; determining a loss function value according to the predicted feature map and the encoded feature map of the next sample point cloud frame following the plurality of adjacent sample point cloud frames; and training the first feature extraction network model according to the loss function value. By means of the steps above, self-supervised learning of a point cloud feature extraction network model is realized, so that the cost of data annotation is reduced, and the performance of a trained feature extraction model is improved.
Inventors:
LIU HAO (CN)
Application Number:
PCT/CN2023/082809
Publication Date:
March 21, 2024
Filing Date:
March 21, 2023
Export Citation:
Assignee:
BEIJING JINGDONG QIANSHI TECH CO LTD (CN)
International Classes:
G06V10/40
Domestic Patent References:
| WO2022105502A1 | 2022-05-27 |
Foreign References:
| CN115482391A | 2022-12-16 | |||
| CN114898355A | 2022-08-12 |
Attorney, Agent or Firm:
CCPIT PATENT AND TRADEMARK LAW OFFICE (CN)
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