Summary of Enhancing Performance Of Point Cloud Completion Networks with Consistency Loss, by Kevin Tirta Wijaya et al.
Enhancing Performance of Point Cloud Completion Networks with Consistency Loss
by Kevin Tirta Wijaya, Christofel Rio Goenawan, Seung-Hyun Kong
First submitted to arxiv on: 9 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 paper proposes a novel approach to enhance the performance of point cloud completion networks by addressing the one-to-many mapping issue that arises when incomplete objects have multiple valid completion solutions. The conventional training objective often produces contradictory supervision signals, which can hinder network optimization. To mitigate this problem, the authors introduce a completion consistency loss function that ensures the generated completion solution is coherent for incomplete objects originating from the same source point cloud. Experimental results demonstrate the effectiveness of the proposed approach, enhancing the performance of various existing networks without modifying their design. The method achieves state-of-the-art accuracy on the challenging MVP dataset and does not affect inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better fill in missing parts of 3D objects. When an object is incomplete, there can be many different ways to finish it correctly. This makes it hard for computers to learn how to do a good job filling in the missing parts. The researchers created a new way for computers to learn by making sure that when they see the same incomplete object, they always fill it in the same way. This helps them get better at doing it correctly and quickly. |
Keywords
» Artificial intelligence » Inference » Loss function » Optimization
