Summary of 1-lipschitz Neural Distance Fields, by Guillaume Coiffier and Louis Bethune
1-Lipschitz Neural Distance Fields
by Guillaume Coiffier, Louis Bethune
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 introduces a new method for approximating the signed distance function of an object using neural implicit surfaces. The authors train a neural network to represent a solid object as the zero level set of its output, which is then used to estimate the signed distance function. This approach exhibits high visual fidelity and quality near the surface but degrades with distance, making it difficult to perform geometrical queries without additional analysis techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to create detailed 3D models of objects from just a few points or lines. It’s like creating a map of an object by guessing where the boundaries are. The model is very good at showing what the object looks like up close, but it gets worse when you look at it from far away. To fix this, the authors created a new way to train the AI that makes sure it never overestimates how far something is from the object’s surface. This means that even if the 3D model is not perfect, the AI can still find the closest point or calculate the distance from the object. |
Keywords
» Artificial intelligence » Neural network
