ToCo-Mesh: Topology-Consistent Dynamic Mesh Reconstruction via Adaptive Tessellation and Surface-Aligned 2DGS

2026-09-01

AI Summary

This study addresses the challenge of simultaneously achieving topological consistency and high-fidelity geometric detail in dynamic mesh reconstruction by proposing a dual-mesh representation architecture. The method enables precise parameterization through barycentric coordinate mapping and surface-aligned 2D Gaussian splatting. Furthermore, it introduces a novel error-driven adaptive subdivision and merging mechanism that supports dynamic resolution adjustment while strictly preserving topological consistency. Experimental results demonstrate that the proposed approach achieves state-of-the-art geometric reconstruction accuracy while maintaining highly competitive photorealistic rendering quality.

Abstract

Reconstructing dynamic meshes with consistent topology from multi-view temporal images remains a challenge. Existing approaches typically face a dilemma between fine-scale shape recovery and topological stability. Frame-by-frame extraction methods capture fine details but break vertex correspondence, leading to flickering meshes. Conversely, template-based deformation ensures consistency but struggles to adapt its surface resolution during optimization, missing local surface details. To address these limitations, we propose ToCo-Mesh, a dynamic reconstruction framework that maintains topology consistency over time while achieving high-fidelity geometry. Specifically, we introduce a dual-mesh representation, where a canonical template mesh is tightly bound to time-varying coarse guide meshes via barycentric parameterization. While keeping guide meshes fixed to condition the deformation, we perform error-driven split-and-merge on the template mesh to progressively increase reconstruction fidelity. Furthermore, to suppress surface irregularities and achieve photorealistic rendering, we incorporate a Surface-Aligned 2DGS module. By anchoring flattened Gaussians to mesh faces, we utilize their rendered normals to guide inverse geometric fine-tuning. To our knowledge, ToCo-Mesh is the first framework to enable adaptive mesh refinement while maintaining strict topological consistency. Extensive experiments demonstrate that our method achieves SOTA geometric accuracy while maintaining competitive rendering quality.

Problem

• Dynamic Mesh Reconstruction
• Topology Consistency
• Multi-view Temporal Images
• Adaptive Surface Resolution

Innovation

• Dynamic Mesh Reconstruction
• Topology Consistency
• Adaptive Tessellation
• Surface-Aligned 2DGS
• Dual-Mesh Representation