Summary of Bayesian Diffusion Models For 3d Shape Reconstruction, by Haiyang Xu et al.
Bayesian Diffusion Models for 3D Shape Reconstruction
by Haiyang Xu, Yu Lei, Zeyuan Chen, Xiang Zhang, Yue Zhao, Yilin Wang, Zhuowen Tu
First submitted to arxiv on: 11 Mar 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 Bayesian Diffusion Models (BDM) are a prediction algorithm that couples prior information with data-driven procedures through joint diffusion processes. This approach improves 3D shape reconstruction by incorporating rich prior knowledge from standalone labels. Unlike traditional Bayesian frameworks, BDM fuses top-down and bottom-up processes via learned gradient computation networks, enabling seamless information exchange and fusion. Our model achieves state-of-the-art results on both synthetic and real-world benchmarks for 3D shape reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BDM is a new way to do predictions that combines what we already know with what the data tells us. It’s good at reconstructing 3D shapes from point clouds, even when there isn’t much data. This works because BDM brings in extra information about what the shape should look like before trying to figure it out from the data. |
Keywords
* Artificial intelligence * Diffusion
