Papers › Stratified Transformer for 3D Point Cloud Segmentation
Stratified Transformer for 3D Point Cloud Segmentation
Xin Lai, Jianhui Liu, Li Jiang, LiWei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia
3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies. In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance. Specifically, we first put forward a novel key sampling strategy. For each query point, we sample nearby points densely and distant points sparsely as its keys in a stratified way, which enables the model to enlarge the effective receptive field and enjoy long-range contexts at a low computational cost. Also, to combat the challenges posed by irregular point arrangements, we propose first-layer point embedding to aggregate local information, which facilitates convergence and boosts performance. Besides, we adopt contextual relative position encoding to adaptively capture position information. Finally, a memory-efficient implementation is introduced to overcome the issue of varying point numbers in each window. Extensive experiments demonstrate the effectiveness and superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets. Code is available at https://github.com/dvlab-research/Stratified-Transformer.
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Code
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Code Syntology ran Syntology
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | S3DIS Area5 | StratifiedTransformer | Number of params | 8.0M | #24 of 61 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | StratifiedTransformer | mAcc | 78.1 | #24 of 61 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | StratifiedTransformer | mIoU | 72.0 | #24 of 61 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | StratifiedTransformer | oAcc | 91.5 | #24 of 61 | Archive leaderboard | report |
| Semantic Segmentation | ScanNet | StratifiedFormer | test mIoU | 73.7 | #23 of 45 | Archive leaderboard | report |
| Semantic Segmentation | ScanNet | StratifiedFormer | val mIoU | 74.3 | #23 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.
Methods
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