Papers › Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis
Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis
Xu Yan, Heshen Zhan, Chaoda Zheng, Jiantao Gao, Ruimao Zhang, Shuguang Cui, Zhen Li
Although recent point cloud analysis achieves impressive progress, the paradigm of representation learning from a single modality gradually meets its bottleneck. In this work, we take a step towards more discriminative 3D point cloud representation by fully taking advantages of images which inherently contain richer appearance information, e.g., texture, color, and shade. Specifically, this paper introduces a simple but effective point cloud cross-modality training (PointCMT) strategy, which utilizes view-images, i.e., rendered or projected 2D images of the 3D object, to boost point cloud analysis. In practice, to effectively acquire auxiliary knowledge from view images, we develop a teacher-student framework and formulate the cross modal learning as a knowledge distillation problem. PointCMT eliminates the distribution discrepancy between different modalities through novel feature and classifier enhancement criteria and avoids potential negative transfer effectively. Note that PointCMT effectively improves the point-only representation without architecture modification. Sufficient experiments verify significant gains on various datasets using appealing backbones, i.e., equipped with PointCMT, PointNet++ and PointMLP achieve state-of-the-art performance on two benchmarks, i.e., 94.4% and 86.7% accuracy on ModelNet40 and ScanObjectNN, respectively. Code will be made available at https://github.com/ZhanHeshen/PointCMT.
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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 Point Cloud Classification | ModelNet40 | PointNet2+PointCMT | Mean Accuracy | 91.2 | #14 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | PointNet2+PointCMT | Number of params | 1.62M | #14 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | PointNet2+PointCMT | Overall Accuracy | 94.4 | #14 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ScanObjectNN | PointCMT | Mean Accuracy | 84.8 | #46 of 77 | Archive leaderboard | report |
| 3D Point Cloud Classification | ScanObjectNN | PointCMT | Number of params | 12.6M | #46 of 77 | Archive leaderboard | report |
| 3D Point Cloud Classification | ScanObjectNN | PointCMT | Overall Accuracy | 86.7 | #46 of 77 | 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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