Mesh Splatting for End-to-end Multiview Surface Reconstruction

2026-01-29

AI Summary

Existing 3D surface reconstruction methods suffer from inherent limitations between volumetric and purely surface-based representations: the former often introduces redundancy and accumulative errors, while the latter, constrained by a single-layer receptive field, struggles to recover complex geometry. This work proposes a differentiable mesh softening strategy that explicitly extends surfaces into a multi-layer translucent volumetric representation with a controllable 3D receptive field. By integrating splatting-based volume rendering with topological optimization, our approach enhances representational capacity and numerical stability while preserving the advantages of surface parameterization. The method enables end-to-end high-quality reconstruction, significantly improving geometric accuracy and mesh quality in approximately 20 minutes, and effectively recovers intricate surface details.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Surfaces are typically represented as meshes, which can be extracted from volumetric fields via meshing or optimized directly as surface parameterizations. Volumetric representations occupy 3D space and have a large effective receptive field along rays, enabling stable and efficient optimization via volumetric rendering; however, subsequent meshing often produces overly dense meshes and introduces accumulated errors. In contrast, pure surface methods avoid meshing but capture only boundary geometry with a single-layer receptive field, making it difficult to learn intricate geometric details and increasing reliance on priors (e.g., shading or normals). We bridge this gap by differentiably turning a surface representation into a volumetric one, enabling end-to-end surface reconstruction via volumetric rendering to model complex geometries. Specifically, we soften a mesh into multiple semi-transparent layers that remain differentiable with respect to the base mesh, endowing it with a controllable 3D receptive field. Combined with a splatting-based renderer and a topology-control strategy, our method can be optimized in about 20 minutes to achieve accurate surface reconstruction while substantially improving mesh quality.

Problem

• surface reconstruction
• mesh representation
• volumetric rendering
• multiview geometry
• mesh quality

Innovation

• Mesh Splatting
• Differentiable Rendering
• Surface Reconstruction
• Volumetric Representation
• Topology Control