Summary of Multiple Rotation Averaging with Constrained Reweighting Deep Matrix Factorization, by Shiqi Li et al.
Multiple Rotation Averaging with Constrained Reweighting Deep Matrix Factorization
by Shiqi Li, Jihua Zhu, Yifan Xie, Naiwen Hu, Mingchen Zhu, Zhongyu Li, Di Wang
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 proposes an effective rotation averaging method for computer vision and robotics domains, which avoids the requirement of ground truth labels in the supervised training process. The conventional optimization-based methods optimize a nonlinear cost function based on noise assumptions, while learning-based methods require labels. This paper uses deep matrix factorization to directly solve the multiple rotation averaging problem in unconstrained linear space, designed for low-rank and symmetric neural networks. A spanning tree-based edge filtering is used to suppress outliers, and reweighting and dynamic depth selection strategies improve robustness. The method combines benefits of optimization-based and learning-based methods. Experimental results on various datasets validate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to find patterns in computer vision and robotics without needing special labels. Most current methods either rely on assumptions about noise or need those labels. This paper uses a special kind of matrix factorization to solve the problem without those requirements. It designs a neural network that is good for this specific task, and then adds filters to remove bad data. The method combines the best of both worlds, and tests show it works well. |
Keywords
» Artificial intelligence » Neural network » Optimization » Supervised
