Point-Selection Fine-Tuning Framework for Robust Point Cloud Classification
Quick Answer
The PSFT framework enhances point cloud classification robustness by selectively retaining minimally influential points, achieving significant error reduction on ModelNet-C and ModelNet40-C benchmarks.
Quick Take
It outperforms existing tuning strategies, particularly with ULIP-2 and Uni3D-B models, demonstrating superior performance against corruption.
Key Points
- PSFT estimates point-wise influence to suppress outliers effectively.
- The framework uses a lightweight feature filter to refine patch-token representations.
- Extensive experiments show consistent error reduction across all tested 3D models.
- Achieved strongest results on ScanObjectNN-C with ULIP-2 and Uni3D-B.
- Implementation details available at the provided arXiv link.
Paper Resources
Source Excerpt
Noisy and corrupted points can substantially degrade point cloud recognition performance, especially under challenging corruption settings. In particular, full fine-tuning of 3D pre-trained models may amplify the influence of outliers and overwrite robustness priors learned during pre-training, while naive parameter-efficient adaptation remains sensitive to corrupted tokens. To address this issue, we propose PSFT, a point-selection fine-tuning framework that improves robustness while remaining p
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