A comparative analysis of point sampling strategies in point-based 3D object detection

Rui Zhu; Kin Fun Li; Chenchen Guo; Jinmin Huang

doi:10.1117/12.3023739

14 March 2024 A comparative analysis of point sampling strategies in point-based 3D object detection

Rui Zhu, Kin Fun Li, Chenchen Guo, Jinmin Huang

Proceedings Volume 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023); 1307408 (2024) https://doi.org/10.1117/12.3023739
Event: Fifth International Conference on Image, Video Processing and Artificial Intelligence, 2023, Shenzhen, China

Abstract

Point-based 3D object detection has been receiving increasing attention as it can preserve the geometric information of a point cloud and avoid quantization errors or information loss caused by voxelization or projection. Point sampling plays an important role in point-based 3D detectors yet has not been thoroughly explored. In this paper, we conduct a comparative analysis of three point sampling strategies to gain a deep understanding of the effect that each strategy imposes on the final performance and intermediate stages of the network. We introduce density-aware sampling and semantic-aware sampling strategies and fit them into the backbone of a lightweight and effective baseline model, aiming to reduce the density imbalance of the point cloud and better utilize semantic information. The density-aware strategy effectively balances the density but the inference time is not suitable for real-time application. Semantic-aware sampling biased on foreground points achieves a 0.19% improvement over the baseline. Analysis on statistics and visualization reveals future research direction. We build our models on the MMDetection3D platform and evaluate performance on the KITTI dataset.

Citation Download Citation

Rui Zhu, Kin Fun Li, Chenchen Guo, and Jinmin Huang "A comparative analysis of point sampling strategies in point-based 3D object detection", Proc. SPIE 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023), 1307408 (14 March 2024); https://doi.org/10.1117/12.3023739

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