Papers › ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point...
ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding
Lunhao Duan, Shanshan Zhao, Nan Xue, Mingming Gong, Gui-Song Xia, DaCheng Tao
Transformers have been recently explored for 3D point cloud understanding with impressive progress achieved. A large number of points, over 0.1 million, make the global self-attention infeasible for point cloud data. Thus, most methods propose to apply the transformer in a local region, e.g., spherical or cubic window. However, it still contains a large number of Query-Key pairs, which requires high computational costs. In addition, previous methods usually learn the query, key, and value using a linear projection without modeling the local 3D geometric structure. In this paper, we attempt to reduce the costs and model the local geometry prior by developing a new transformer block, named ConDaFormer. Technically, ConDaFormer disassembles the cubic window into three orthogonal 2D planes, leading to fewer points when modeling the attention in a similar range. The disassembling operation is beneficial to enlarging the range of attention without increasing the computational complexity, but ignores some contexts. To provide a remedy, we develop a local structure enhancement strategy that introduces a depth-wise convolution before and after the attention. This scheme can also capture the local geometric information. Taking advantage of these designs, ConDaFormer captures both long-range contextual information and local priors. The effectiveness is demonstrated by experimental results on several 3D point cloud understanding benchmarks. Code is available at https://github.com/LHDuan/ConDaFormer .
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | S3DIS Area5 | ConDaFormer | mAcc | 78.9 | #9 of 61 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | ConDaFormer | mIoU | 73.5 | #9 of 61 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | ConDaFormer | oAcc | 92.4 | #9 of 61 | 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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