Papers › 3D Point Capsule Networks
3D Point Capsule Networks
Yongheng Zhao, Tolga Birdal, Haowen Deng, Federico Tombari
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Classification | ModelNet40 | 3D-PointCapsNet | Classification Accuracy | 89.3 | #5 of 7 | Archive leaderboard | report |
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