Comparative Analysis of Point Sampling Strategies in Point-based 3D Object Detection




Zhu, Rui

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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 research, 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 applicable for real-time application. Semantic-aware sampling biased on foreground points achieves a 0.19\% improvement on the baseline. Analysis on statistics and visualization reveals future research direction. We build our models on MMDetection3D platform and evaluate the performance on KITTI dataset.



3D object detection, point sampling, point cloud, deep learning