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DMesh: A Differentiable Mesh Representation

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arxiv 2404.13445 v3 pith:OLXZ3KKB submitted 2024-04-20 cs.CV cs.GR

DMesh: A Differentiable Mesh Representation

Sanghyun Son , Matheus Gadelha , Yang Zhou , Zexiang Xu , Ming C. Lin , Yi Zhou This is my paper
classification cs.CV cs.GR
keywords differentiabledmeshmeshfacesmeshesrepresentationtetrahedratriangular
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We present a differentiable representation, DMesh, for general 3D triangular meshes. DMesh considers both the geometry and connectivity information of a mesh. In our design, we first get a set of convex tetrahedra that compactly tessellates the domain based on Weighted Delaunay Triangulation (WDT), and select triangular faces on the tetrahedra to define the final mesh. We formulate probability of faces to exist on the actual surface in a differentiable manner based on the WDT. This enables DMesh to represent meshes of various topology in a differentiable way, and allows us to reconstruct the mesh under various observations, such as point cloud and multi-view images using gradient-based optimization. The source code and full paper is available at: https://sonsang.github.io/dmesh-project.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. PolyFlow: Continuous Topology Embedding Flow Matching for Artist-style Mesh Generation

    cs.GR 2026-06 unverdicted novelty 7.0

    PolyFlow converts discrete meshes to continuous per-vertex representations using a topology embedder and applies flow matching for parallel artist-style mesh generation that outperforms autoregressive baselines on Toy...

  2. ExMesh: EXplicit Mesh Reconstruction with Topology Adaptation

    cs.CV 2026-06 unverdicted novelty 7.0

    ExMesh introduces a framework for explicit mesh reconstruction from images that integrates adaptive topology updates into differentiable optimization while maintaining UV coordinates.

  3. MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer

    cs.CV 2026-06 unverdicted novelty 6.0

    MeshFlow uses a contrastive MeshVAE for compact mesh latents and a flow transformer for parallel generation, claiming 18x speedup over autoregressive methods with high accuracy on standard metrics.