SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds
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arXiv
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| Format: | Preprint |
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2025
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| author | Bahri, Ali Yazdanpanah, Moslem Dastani, Sahar Noori, Mehrdad Hakim, Gustavo Adolfo Vargas Osowiechi, David Beizaee, Farzad Ayed, Ismail Ben Desrosiers, Christian |
| author_facet | Bahri, Ali Yazdanpanah, Moslem Dastani, Sahar Noori, Mehrdad Hakim, Gustavo Adolfo Vargas Osowiechi, David Beizaee, Farzad Ayed, Ismail Ben Desrosiers, Christian |
| contents | Test-Time Training (TTT) has emerged as a promising solution to address distribution shifts in 3D point cloud classification. However, existing methods often rely on computationally expensive backpropagation during adaptation, limiting their applicability in real-world, time-sensitive scenarios. In this paper, we introduce SMART-PC, a skeleton-based framework that enhances resilience to corruptions by leveraging the geometric structure of 3D point clouds. During pre-training, our method predicts skeletal representations, enabling the model to extract robust and meaningful geometric features that are less sensitive to corruptions, thereby improving adaptability to test-time distribution shifts. Unlike prior approaches, SMART-PC achieves real-time adaptation by eliminating backpropagation and updating only BatchNorm statistics, resulting in a lightweight and efficient framework capable of achieving high frame-per-second rates while maintaining superior classification performance. Extensive experiments on benchmark datasets, including ModelNet40-C, ShapeNet-C, and ScanObjectNN-C, demonstrate that SMART-PC achieves state-of-the-art results, outperforming existing methods such as MATE in terms of both accuracy and computational efficiency. The implementation is available at: https://github.com/AliBahri94/SMART-PC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19546 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds Bahri, Ali Yazdanpanah, Moslem Dastani, Sahar Noori, Mehrdad Hakim, Gustavo Adolfo Vargas Osowiechi, David Beizaee, Farzad Ayed, Ismail Ben Desrosiers, Christian Computer Vision and Pattern Recognition Test-Time Training (TTT) has emerged as a promising solution to address distribution shifts in 3D point cloud classification. However, existing methods often rely on computationally expensive backpropagation during adaptation, limiting their applicability in real-world, time-sensitive scenarios. In this paper, we introduce SMART-PC, a skeleton-based framework that enhances resilience to corruptions by leveraging the geometric structure of 3D point clouds. During pre-training, our method predicts skeletal representations, enabling the model to extract robust and meaningful geometric features that are less sensitive to corruptions, thereby improving adaptability to test-time distribution shifts. Unlike prior approaches, SMART-PC achieves real-time adaptation by eliminating backpropagation and updating only BatchNorm statistics, resulting in a lightweight and efficient framework capable of achieving high frame-per-second rates while maintaining superior classification performance. Extensive experiments on benchmark datasets, including ModelNet40-C, ShapeNet-C, and ScanObjectNN-C, demonstrate that SMART-PC achieves state-of-the-art results, outperforming existing methods such as MATE in terms of both accuracy and computational efficiency. The implementation is available at: https://github.com/AliBahri94/SMART-PC. |
| title | SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.19546 |