Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging

Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, Sahar Dastani Oghani, Milad Cheraghalikhani, David Osowiechi, Farzad Beizaee, Gustavo A. Vargas Hakim, Ismail Ben Ayed, Christian Desrosiers; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 266-275

Abstract

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. The implementation is available at: https://github.com/AliBahri94/SVWA_TTA.git.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Bahri_2025_WACV, author = {Bahri, Ali and Yazdanpanah, Moslem and Noori, Mehrdad and Oghani, Sahar Dastani and Cheraghalikhani, Milad and Osowiechi, David and Beizaee, Farzad and Hakim, Gustavo A. Vargas and Ben Ayed, Ismail and Desrosiers, Christian}, title = {Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {266-275} }