Summary of Enhancing Diffusion-based Point Cloud Generation with Smoothness Constraint, by Yukun Li et al.
Enhancing Diffusion-based Point Cloud Generation with Smoothness Constraint
by Yukun Li, Liping Liu
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); 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 This paper proposes a new approach to point cloud generation using diffusion models. The existing methods use forward and reverse diffusion processes to convert the original point distribution into noise and then recover the point distribution from the noise. However, these approaches can produce non-smooth points on the surface due to ignoring the geometric properties of the point clouds. To alleviate this issue, the authors incorporate a local smoothness constraint into the diffusion framework. The experiments demonstrate that the proposed model generates realistic shapes and smoother point clouds, outperforming multiple state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to create 3D models from points in space. Right now, these models can have rough edges because we’re not paying attention to how the points fit together. The authors came up with a new way to make these models that takes into account how the points are arranged. They used something called diffusion models to create smoother, more realistic shapes. |
Keywords
* Artificial intelligence * Attention * Diffusion
