Distance Field Rasterization for End-to-End Mesh Reconstruction

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 surface extraction to heuristic post-processing. In contrast, implicit signed distance field (SDF) methods provide well-defined surfaces but are typically optimized with computationally expensive ray marching. We propose SDFRaster, a rasterizable SDF representation that bridges this gap by combining the efficiency of rasterization with signed distance field for end-to-end mesh reconstruction. Starting from a Delaunay tetrahedralization, we optimize 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, enabling end-to-end mesh reconstruction without post-processing mesh extraction. Experiments on DTU and Tanks and Temples demonstrate that SDFRaster achieves higher-quality and more complete surface reconstructions with lower storage cost than state-of-the-art approaches. Project page: ustc3dv/SDFRaster