Papers › Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point...
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation
Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information, makes point cloud recognition challenging. Recent edge-aware methods incorporate edge modeling into network designs to better describe local structures. Although these methods show that incorporating edge information is beneficial, how edge information helps remains unclear, making it difficult for users to analyze its usefulness. To shed light on this issue, in this study, we propose a new algorithm called Diffusion Unit (DU) that handles edge information in a principled and interpretable manner while providing decent improvement. First, we theoretically show that DU learns to perform task-beneficial edge enhancement and suppression. Second, we experimentally observe and verify the edge enhancement and suppression behavior. Third, we empirically demonstrate that this behavior contributes to performance improvement. Extensive experiments and analyses performed on challenging benchmarks verify the effectiveness of DU. Specifically, our method achieves state-of-the-art performance in object part segmentation using ShapeNet part and scene segmentation using S3DIS. Our source code is available at https://github.com/martianxiu/DiffusionUnit.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Part Segmentation | ShapeNet-Part | Diffusion Unit | Class Average IoU | 85.2 | #6 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | Diffusion Unit | Instance Average IoU | 87.1 | #6 of 67 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | Diffusion Unit | Number of params | 8.1M | #22 of 61 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | Diffusion Unit | mAcc | 78.1 | #22 of 61 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | Diffusion Unit | mIoU | 72.2 | #22 of 61 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | Diffusion Unit | oAcc | 91.3 | #22 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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