ExMesh: Explicit Mesh Reconstruction with Topology Adaptation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
ToCo-Mesh is a dynamic mesh reconstruction framework that preserves topology consistency while achieving high‑fidelity geometry from multi‑view temporal images. It uses a dual‑mesh representation, coupling a canonical template mesh to time‑varying coarse guide meshes via barycentric parameterization, and applies error‑driven split‑and‑merge operations on the template to refine detail. A Surface‑Aligned 2DGS module anchors flattened Gaussians to mesh faces, using rendered normals to guide fine‑tuning and suppress surface irregularities, resulting in photorealistic rendering.
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Generating high-quality triangle meshes is essential for film, gaming, and interactive 3D applications. Mainstream methods rely on mesh serialization and autoregressive processes, which stuggles in effective inference and is sensitive to error accumulation.
MeshOctave introduces a novel approach to native mesh generation that replaces sequential autoregressive or heuristic flow models with a parallel, coarse-to-fine paradigm. By defining scales through dyadic spatial grid resolutions, it collapses vertices deterministically and then performs split-and-rewire operations to reconstruct fine topology without serialization. The method employs a scale-conditioned masked-uniform discrete diffusion model, achieving higher geometric fidelity and topological validity while supporting adaptive resolution refinement and mesh subdivision tasks.
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