Distance Field Rasterization for End-to-End Mesh Reconstruction

2026-04-26

AI Summary

This work addresses the limitations of existing rasterization-based methods, which lack a globally consistent explicit surface representation, and traditional signed distance field (SDF) approaches that rely on computationally expensive ray marching for mesh reconstruction. The paper introduces SDFRaster, the first method to combine the efficiency of rasterization with the geometric clarity of SDFs. It optimizes a continuous SDF over a Delaunay tetrahedral grid and enables efficient rendering through tetrahedral rasterization and alpha blending. Furthermore, it incorporates a differentiable Marching Tetrahedra algorithm to support end-to-end mesh reconstruction. Without requiring post-processing, SDFRaster produces consistent, watertight triangle meshes, achieving higher-quality and more complete reconstructions on the DTU and Tanks and Temples benchmarks while reducing memory overhead.

Technology Category

• Computer Vision: 3D Computer Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Rasterization based methods have recently enabled high-quality novel view synthesis at real-time rates, but their underlying volumetric primitives do not expose a direct, globally consistent surface representation, leaving sur face extraction to heuristic post-processing. In contrast, implicit signed dis tance field (SDF) methods provide well-defined surfaces but are typically optimized with computationally expensive ray marching. We propose SD FRaster, a rasterizable SDF representation that bridges this gap by combin ing the efficiency of rasterization with signed distance field for end-to-end mesh reconstruction. Starting from a Delaunay tetrahedralization, we op timize a continuous SDF over a tetrahedral grid and render it efficiently by rasterizing tetrahedra and alpha-compositing their contributions. We further integrate differentiable Marching Tetrahedra into the optimization loop, enablingend-to-endmeshreconstructionwithoutpost-processingmesh extraction. Experiments on DTU and Tanks and Temples demonstrate that SDFRaster achieves higher-quality and more complete surface reconstruc tions with lower storage cost than state-of-the-art approaches. Project page: https://ustc3dv.github.io/SDFRaster/

Problem

• mesh reconstruction
• signed distance field
• rasterization
• surface representation
• end-to-end

Innovation

• Signed Distance Field
• Rasterization
• Differentiable Rendering
• End-to-End Mesh Reconstruction
• Marching Tetrahedra