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

Source: [Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging](<https://www.sota2.com/research/paper/68fa76731e209131cea6d161>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:45:57.319Z

Authors: Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, Sahar Dastani, Milad Cheraghalikhani, David Osowiech, Farzad Beizaee, Gustavo adolfo.vargas-hakim, Ismail Ben Ayed, Christian Desrosiers

Published: 2024-11-02T02:59:25.000Z

## 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.

## 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](<https://www.sota2.com/research/sota/3d-point-cloud-classification-on-shapenet-c.md>): 3D Point Cloud Classification; ShapeNet-C; Accuracy (Uniform Noise): 82.04.

- [3D Point Cloud Classification on ScanObjectNN-C 1.0 (test)](<https://www.sota2.com/research/sota/3d-point-cloud-classification-on-scanobjectnn-c-1-0-test.md>): 3D Point Cloud Classification; ScanObjectNN-C 1.0 (test); Accuracy (Uniform Noise): 44.62.

- [3D Point Cloud Classification on ModelNet40-C (test)](<https://www.sota2.com/research/sota/3d-point-cloud-classification-on-modelnet40-c-test.md>): 3D Point Cloud Classification; ModelNet40-C (test); Robustness to Rotation: 67.33.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2411.01116>)

[PDF](<https://arxiv.org/pdf/2411.01116.pdf>)