Papers › AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding
AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding
Hongcheng Yang, Dingkang Liang, Dingyuan Zhang, Zhe Liu, Zhikang Zou, Xingyu Jiang, Yingying Zhu
The recent advancements in point cloud learning have enabled intelligent vehicles and robots to comprehend 3D environments better. However, processing large-scale 3D scenes remains a challenging problem, such that efficient downsampling methods play a crucial role in point cloud learning. Existing downsampling methods either require a huge computational burden or sacrifice fine-grained geometric information. For such purpose, this paper presents an advanced sampler that achieves both high accuracy and efficiency. The proposed method utilizes voxel centroid sampling as a foundation but effectively addresses the challenges regarding voxel size determination and the preservation of critical geometric cues. Specifically, we propose a Voxel Adaptation Module that adaptively adjusts voxel sizes with the reference of point-based downsampling ratio. This ensures that the sampling results exhibit a favorable distribution for comprehending various 3D objects or scenes. Meanwhile, we introduce a network compatible with arbitrary voxel sizes for sampling and feature extraction while maintaining high efficiency. The proposed approach is demonstrated with 3D object detection and 3D semantic segmentation. Compared to existing state-of-the-art methods, our approach achieves better accuracy on outdoor and indoor large-scale datasets, e.g. Waymo and ScanNet, with promising efficiency.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | Waymo Open Dataset | AVS-Net | mAPH/L2 | 72.4 | #3 of 8 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | AVS-Net | Class Average IoU | 85.7 | #3 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | AVS-Net | Instance Average IoU | 87.3 | #3 of 67 | Archive leaderboard | report |
| Semantic Segmentation | ScanNet | AVS-Net | val mIoU | 76.1 | #15 of 45 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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