# SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds Source: [SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:46:07.791Z Authors: Ali Bahri, Moslem Yazdanpanah, Sahar Dastani, Mehrdad Noori, Gustavo Adolfo Vargas Hakim, David Osowiechi, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers Published: 2025-05-26T06:11:02.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Point Cloud Classification on ShapeNet-C](): 3D Point Cloud Classification; ShapeNet-C; Accuracy (Uniform Noise): 80.8. - [3D Point Cloud Classification on ScanObjectNN-C 1.0 (test)](): 3D Point Cloud Classification; ScanObjectNN-C 1.0 (test); Accuracy (Uniform Noise): 27.5. - [3D Point Cloud Classification on ModelNet40-C (test)](): 3D Point Cloud Classification; ModelNet40-C (test); Robustness to Rotation: 43.9. ## Original sources and implementation [Original paper]() [PDF]()