3D point cloud target detection algorithm based on dynamic convolution and adaptive pooling

Binbin Shen; Liming Cai; Qingguo Chi; Yi Su; Bo Zhang; Liang Chen

doi:10.1117/12.3029352

22 May 2024 3D point cloud target detection algorithm based on dynamic convolution and adaptive pooling

Binbin Shen, Liming Cai, Qingguo Chi, Yi Su, Bo Zhang, Liang Chen

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760S (2024) https://doi.org/10.1117/12.3029352
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

This paper proposes a 3D object detection algorithm that combines dynamic convolution with adaptive pooling as extraction (PAC-PAPRCNN). The algorithm is divided into two stages. The first stage generates 3D feature proposals in a bottom-up manner, and the second stage refines the proposals generated in the first stage. Firstly, in the first stage, a new type of dynamic convolution is used to adaptively learn the location features of points. To classify foreground and background points in the pooling layer, adaptive pooling theory is applied. Meanwhile, the extracted foreground points are used as regression boxes one by one, and then the regression box with the best score is selected by non-maximum suppression. Secondly, in the second stage, based on the first stage, granularity is refined to obtain a 3D regression box of revised specification, thus achieving 3D object detection. Finally, a series of experimental results on the KITTI dataset demonstrate that the proposed method is reasonable and effective.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Binbin Shen, Liming Cai, Qingguo Chi, Yi Su, Bo Zhang, and Liang Chen "3D point cloud target detection algorithm based on dynamic convolution and adaptive pooling", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760S (22 May 2024); https://doi.org/10.1117/12.3029352

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KEYWORDS

Point clouds

3D acquisition

Convolution

Matrices

Object detection

Education and training

Feature extraction

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