Summary of Spacemesh: a Continuous Representation For Learning Manifold Surface Meshes, by Tianchang Shen et al.
SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes
by Tianchang Shen, Zhaoshuo Li, Marc Law, Matan Atzmon, Sanja Fidler, James Lucas, Jun Gao, Nicholas Sharp
First submitted to arxiv on: 30 Sep 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 scheme presents a neural network that generates manifold, polygonal meshes of complex connectivity by defining a continuous latent connectivity space at each vertex. The key innovation is the use of vertex embeddings that guarantee edge-manifoldness and enable the representation of general polygonal meshes. This approach is well-suited to machine learning and stochastic optimization, without restrictions on connectivity or topology. The paper explores the basic properties of this representation and uses it to fit distributions of meshes from large datasets. The resulting models generate diverse meshes with tessellation structure learned from the dataset population, with concise details and high-quality mesh elements. In applications, this approach not only yields high-quality outputs from generative models but also enables directly learning challenging geometry processing tasks such as mesh repair. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn to create complex shapes by using a new way to represent 3D meshes. Currently, most machine learning methods don’t work directly with meshes, instead treating them as simple collections of triangles or using indirect representations. This paper changes that by creating a neural network that can generate and modify mesh structures directly. The approach is flexible and can be used for tasks like generating new shapes, repairing damaged ones, or even learning how to create complex shapes from scratch. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Optimization
