# DMesh: A Differentiable Mesh Representation Source: [DMesh: A Differentiable Mesh Representation]() Collection index: [llms.txt]() Data updated: 2026-01-02T10:47:30.007Z Authors: Sanghyun Son, Matheus Gadelha, Yang Zhou, Zexiang Xu, Ming C. Lin, Yi Zhou Published: 2024-04-20T18:52:51.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Point Cloud Reconstruction on Thingi32, DeepFashion3D, and Objaverse (test)](): Point Cloud Reconstruction; Thingi32, DeepFashion3D, and Objaverse (test); F1: 88.6. - [Multi-View Reconstruction on Thingi32, DeepFashion3D, and Objaverse (test)](): Multi-View Reconstruction; Thingi32, DeepFashion3D, and Objaverse (test); Chamfer Distance: 0.000346. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()