Test-Time Adaptation (TTA) addresses distribution shifts during testing by adapting a pretrained model without access to source data. In this work, we propose a novel TTA approach for 3D point cloud classification, combining sampling variation with weight averaging. Our method leverages Farthest Point Sampling (FPS) and K-Nearest Neighbors (KNN) to create multiple point cloud representations, adapting the model for each variation using the TENT algorithm. The final model parameters are obtained by averaging the adapted weights, leading to improved robustness against distribution shifts. Extensive experiments on ModelNet40-C, ShapeNet-C, and ScanObjectNN-C datasets, with different backbones (Point-MAE, PointNet, DGCNN), demonstrate that our approach consistently outperforms existing methods while maintaining minimal resource overhead. The proposed method effectively enhances model generalization and stability in challenging real-world conditions.

本文研究了测试时自适应（TTA）在模型测试过程中应对分布变化的问题，提出了一种新颖的针对3D点云分类的TTA方法，结合了采样变异和权重平均。通过多个数据集的实验，结果显示该方法在提高模型鲁棒性和稳定性方面优于现有方法，尤其在复杂的实际应用场景中表现出色。

点云中的测试时自适应：利用采样变化与权重平均