Summary of Dapointr: Domain Adaptive Point Transformer For Point Cloud Completion, by Yinghui Li et al.
DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion
by Yinghui Li, Qianyu Zhou, Jingyu Gong, Ye Zhu, Richard Dazeley, Xinkui Zhao, Xuequan Lu
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper focuses on developing a novel framework for point cloud completion, called Domain Adaptive Point Transformer (DAPoinTr), which addresses the problem of limited domain adaptation in point Transformer-based models. The proposed framework consists of three key components: Domain Query-based Feature Alignment (DQFA), Point Token-wise Feature alignment (PTFA), and Voted Prediction Consistency (VPC). DQFA aims to narrow global domain gaps by using a domain proxy and query, while PTFA closes local domain shifts by aligning point proxies and dynamic queries. VPC ensembles predictions from multiple Transformer decoders as experts for voting and pseudo-label generation. The paper demonstrates the effectiveness of DAPoinTr on several domain adaptation benchmarks, outperforming state-of-the-art methods. Code will be publicly available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about improving computers’ ability to complete incomplete point cloud data from different sources. They propose a new approach called Domain Adaptive Point Transformer (DAPoinTr) that helps machines better adapt to new, unseen data. The method consists of three main parts: aligning features, aligning tokens, and combining predictions. This allows the computer to better predict missing points in incomplete point clouds. The researchers tested their approach on several datasets and showed it outperforms current methods. |
Keywords
» Artificial intelligence » Alignment » Domain adaptation » Token » Transformer
