Summary of Loss Distillation Via Gradient Matching For Point Cloud Completion with Weighted Chamfer Distance, by Fangzhou Lin et al.
Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance
by Fangzhou Lin, Haotian Liu, Haoying Zhou, Songlin Hou, Kazunori D Yamada, Gregory S. Fischer, Yanhua Li, Haichong K. Zhang, Ziming Zhang
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 A novel approach to point cloud completion is proposed, leveraging weighted training losses that require no parameter tuning. The researchers develop a search scheme, Loss Distillation via Gradient Matching, to find effective loss functions by mimicking the learning behavior between Chamfer distance and its variants. A bilevel optimization formula is also introduced to train the backbone network using the weighted CD loss. Experimental results show that the Landau weighted CD can outperform HyperCD for point cloud completion, achieving new state-of-the-art results on several benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Point clouds help robots understand environments, but incomplete data can lead to poor results. A new way to improve point cloud completion uses special training losses that don’t need tuning. The approach involves finding the right loss function by mimicking how the model learns. This leads to a novel optimization formula and better results on benchmark datasets. |
Keywords
» Artificial intelligence » Distillation » Loss function » Optimization
