Summary of Curvature Diversity-driven Deformation and Domain Alignment For Point Cloud, by Mengxi Wu et al.
Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud
by Mengxi Wu, Hao Huang, Yi Fang, Mohammad Rostami
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Curvature Diversity-Driven Nuclear-Norm Wasserstein Domain Alignment (CDND) method tackles the significant challenge of bridging the domain gap in Unsupervised Domain Adaptation (UDA) for point cloud data. CDND introduces two key components: a Curvature Diversity-driven Deformation Reconstruction (CurvRec) task and a Deformation-based Nuclear-norm Wasserstein Discrepancy (D-NWD). The former enables models to extract salient features from semantically rich regions, while the latter aligns the source and target domains using Nuclear-norm Wasserstein Discrepancy. A theoretical justification for D-NWD’s effectiveness in distribution alignment is also provided. Experiments on public domain adaptation datasets for point cloud classification and segmentation tasks demonstrate CDND’s state-of-the-art performance, surpassing existing approaches by a noticeable margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CDND is an innovative approach to Unsupervised Domain Adaptation (UDA) that helps computers learn from data without needing much human help. The goal is to make sure the model works well on new, unseen data even if it was trained on very different data before. CDND does this by changing the way data looks and comparing it to the original data using a special formula called Nuclear-norm Wasserstein Discrepancy. This helps the model understand what’s important in the data and ignore things that aren’t useful. The results show that CDND is much better than other methods at adapting to new data. |
Keywords
» Artificial intelligence » Alignment » Classification » Domain adaptation » Unsupervised
