Improved voxel-based point cloud feature extraction algorithm

Wei Cheng; Taiping Xiong; Liqing Shi

doi:10.1117/12.3029338

22 May 2024 Improved voxel-based point cloud feature extraction algorithm

Wei Cheng, Taiping Xiong, Liqing Shi

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760O (2024) https://doi.org/10.1117/12.3029338
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

In previous voxel-based object detection models, the Point Cloud Feature Extraction module (VFE) overlooked the uneven distribution of points within voxels, and as a result, it did not address the issues caused by non-uniform sampling of point clouds. Recognizing this limitation, this paper introduces a new module, VFEPlus, to enhance the feature extraction process. VFEPlus utilizes kernel density estimation and nonlinear transformations to extract the inverse density factors from the point cloud data. This allows for the extraction of point cloud features with density information, thus improving the detection performance that was previously compromised by the non-uniform distribution of point clouds. Experimental comparisons on publicly available datasets demonstrate that this module achieves higher accuracy.

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

Citation Download Citation

Wei Cheng, Taiping Xiong, and Liqing Shi "Improved voxel-based point cloud feature extraction algorithm", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760O (22 May 2024); https://doi.org/10.1117/12.3029338

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KEYWORDS

Point clouds

Voxels

Object detection

Feature extraction

3D image processing

Autonomous driving

Convolution

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