SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds

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:this https URL.

@article{bahri2025_2505.19546,
  title={ SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds },
  author={ Ali Bahri and Moslem Yazdanpanah and Sahar Dastani and Mehrdad Noori and Gustavo Adolfo Vargas Hakim and David Osowiechi and Farzad Beizaee and Ismail Ben Ayed and Christian Desrosiers },
  journal={arXiv preprint arXiv:2505.19546},
  year={ 2025 }
}