Papers › Spherical Transformer for LiDAR-based 3D Recognition
Spherical Transformer for LiDAR-based 3D Recognition
Xin Lai, Yukang Chen, Fanbin Lu, Jianhui Liu, Jiaya Jia
LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from information disconnection and limited receptive field, especially for the sparse distant points. In this work, we study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones. We design radial window self-attention that partitions the space into multiple non-overlapping narrow and long windows. It overcomes the disconnection issue and enlarges the receptive field smoothly and dramatically, which significantly boosts the performance of sparse distant points. Moreover, to fit the narrow and long windows, we propose exponential splitting to yield fine-grained position encoding and dynamic feature selection to increase model representation ability. Notably, our method ranks 1st on both nuScenes and SemanticKITTI semantic segmentation benchmarks with 81.9% and 74.8% mIoU, respectively. Also, we achieve the 3rd place on nuScenes object detection benchmark with 72.8% NDS and 68.5% mAP. Code is available at https://github.com/dvlab-research/SphereFormer.git.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Semantic Segmentation | SemanticKITTI | SphereFormer | test mIoU | 74.8% | #4 of 45 | Archive leaderboard | report |
| 3D Semantic Segmentation | SemanticKITTI | SphereFormer | val mIoU | 67.8% | #4 of 45 | Archive leaderboard | report |
| 3D Semantic Segmentation | WildScenes | SphereFormer | mIoU | 33.97 | #4 of 4 | Archive leaderboard | report |
| LIDAR Semantic Segmentation | S.MID | SphereFormer | val mIoU | 67.8% | #3 of 4 | Archive leaderboard | report |
| LIDAR Semantic Segmentation | nuScenes | SphereFormer | test mIoU | 0.819 | #5 of 36 | Archive leaderboard | report |
| LIDAR Semantic Segmentation | nuScenes | SphereFormer | val mIoU | 0.795 | #5 of 36 | Archive leaderboard | report |
| Semantic Segmentation | KITTI Semantic Segmentation | RPVNet [xu2021rpvnet] | Mean IoU (class) | 80.7 | #1 of 7 | 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.
Methods
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