TopoMesh: High-Fidelity Mesh Autoencoding via Topological Unification

Abstract

The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE's reconstruction capability sets a firm upper bound on generation quality. A fundamental challenge limiting existing VAEs is the representation mismatch between ground-truth meshes and network predictions: GT meshes have arbitrary, variable topology, while VAEs typically predict fixed-structure implicit fields (\eg, SDF on regular grids). This inherent misalignment prevents establishing explicit mesh-level correspondences, forcing prior work to rely on indirect supervision signals such as SDF or rendering losses. Consequently, fine geometric details, particularly sharp features, are poorly preserved during reconstruction. To address this, we introduce TopoMesh, a sparse voxel-based VAE that unifies both GT and predicted meshes under a shared Dual Marching Cubes (DMC) topological framework. Specifically, we convert arbitrary input meshes into DMC-compliant representations via a remeshing algorithm that preserves sharp edges using an L$\infty$ distance metric. Our decoder outputs meshes in the same DMC format, ensuring that both predicted and target meshes share identical topological structures. This establishes explicit correspondences at the vertex and face level, allowing us to derive explicit mesh-level supervision signals for topology, vertex positions, and face orientations with clear gradients. Our sparse VAE architecture employs this unified framework and is trained with Teacher Forcing and progressive resolution training for stable and efficient convergence. Extensive experiments demonstrate that TopoMesh significantly outperforms existing VAEs in reconstruction fidelity, achieving superior preservation of sharp features and geometric details.

Figures (11)

• Figure 1: High-fidelity mesh at $1024^3$ resolution reconstructed by TopoMesh within 5s.
• Figure 2: TopoMesh comprises two core modules. Topo-Remesh converts imperfect, real-world meshes into clean, DMC-compliant representations while preserving sharp features. Topo-VAE takes vertices with normals as input and reconstructs the mesh in the same DMC format. With topological unification, we apply direct supervision on mesh attributes: topology, vertex positions, and face orientations.
• Figure 3: (a) Compression of the sparse attention map. (b) A single, shared query aggregates all keys from the same voxel to form the output feature of this voxel.
• Figure 4: Illustration of explicit mesh-level loss.
• Figure 5: (a) Angle-preserving of L$\infty$ metric. (b) 2D illustration of L$\infty$ distance. $P$ lies on the isosurface that preserves angles.