Summary of Stitch: Surface Reconstruction Using Implicit Neural Representations with Topology Constraints and Persistent Homology, by Anushrut Jignasu et al.
STITCH: Surface reconstrucTion using Implicit neural representations with Topology Constraints and persistent Homology
by Anushrut Jignasu, Ethan Herron, Zhanhong Jiang, Soumik Sarkar, Chinmay Hegde, Baskar Ganapathysubramanian, Aditya Balu, Adarsh Krishnamurthy
First submitted to arxiv on: 24 Dec 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 The paper introduces STITCH, a novel neural network-based method for reconstructing 3D surfaces from sparse point clouds while enforcing topological constraints. The approach uses persistent homology to formulate loss terms that preserve the topology of complex geometries. Experimental results demonstrate excellent performance in preserving topology and shape, with visual and empirical comparisons showcasing the effectiveness of the method. The paper also provides a theoretical analysis, showing that stochastic gradient descent optimizes the loss and enables reconstructing shapes with a single connected component. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary STITCH is a new way to build 3D shapes from points in space. It makes sure the shape has no holes or gaps by using special math tools called persistent homology. This method works really well, making sure complex shapes look right and stay that way. The researchers tested it and showed how it can be used to create shapes with a single connected part. |
Keywords
* Artificial intelligence * Neural network * Stochastic gradient descent
