DCCVT: Differentiable Clipped Centroidal Voronoi Tessellation

Wylliam Cantin Charawi, Adrien Gruson, Jane Wu, Christian Desrosiers, Diego Thomas

Keywords: CVT, 3D reconstruction, SDF, Voronoi diagrams, Delaunay Triangulation

TL;DR: We derive a fully differentiable formulation for computing clipped CVTs and demonstrate its integration with deep learning-based SDF estimation to reconstruct accurate 3D meshes from input point clouds.

Abstract:

While Marching Cubes (MC) and Marching Tetrahedra
(MTet) are widely adopted in 3D reconstruction pipelines
due to their simplicity and efficiency, their differentiable
variants remain suboptimal for mesh extraction. This often limits the quality of 3D meshes reconstructed from point
clouds or images in learning-based frameworks. In contrast, clipped CVTs offer stronger theoretical guarantees
and yield higher-quality meshes. However, the lack of a
differentiable formulation has prevented their integration
into modern machine learning pipelines. To bridge this
gap, we propose DCCVT, a differentiable algorithm that extracts high-quality 3D meshes from noisy signed distance
fields (SDFs) using clipped CVTs. We derive a fully differentiable formulation for computing clipped CVTs and
demonstrate its integration with deep learning-based SDF
estimation to reconstruct accurate 3D meshes from input
point clouds. Our experiments with synthetic data demonstrate the superior ability of DCCVT against state-of-theart methods in mesh quality and reconstruction fidelity.
https://wylliamcantincharawi.dev/DCCVT.github.io/

Submission Number: 384