Summary of Dudf: Differentiable Unsigned Distance Fields with Hyperbolic Scaling, by Miguel Fainstein et al.
DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling
by Miguel Fainstein, Viviana Siless, Emmanuel Iarussi
First submitted to arxiv on: 14 Feb 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 proposes a novel method for training neural networks to approximate unsigned distance fields (UDFs) for 3D reconstruction, addressing the challenge of open surface representation. By learning a hyperbolic scaling of UDFs, the authors formulate a new Eikonal problem with distinct boundary conditions, enabling seamless integration with state-of-the-art implicit neural representation networks. The approach demonstrates significant improvements in reconstruction quality and training performance, while also unlocking the computation of essential topological properties like normal directions and curvatures. Extensive experiments validate the method across various datasets and against competitive baselines, showing enhanced accuracy and up to an order of magnitude increase in speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers understand 3D shapes better. Right now, computers have trouble understanding open surfaces, which are like holes or gaps in objects. The authors came up with a clever idea to fix this problem by scaling the distance fields that describe these surfaces. This allows computers to represent these surfaces more accurately and quickly. The new method also helps computers calculate important details about shapes, like what direction is “up” or how curved something is. The authors tested their approach on many different objects and showed it works better than other methods. |
