用显式Voronoi图+隐式SDF融合，提升三维重建精度与拓扑质量。
SDFoam: Signed-Distance Foam for explicit surface reconstruction
- 结合显式Voronoi图与隐式SDF，通过射线追踪联合优化场景
- 在多种场景下显著降低切比雪夫距离（Chamfer distance），拓扑更准确
- 保留辐射场质量与训练速度，适合需要精确网格重建的应用
神经辐射场（NeRF）通过射线追踪体渲染推动了视图合成的发展。基于点云的3D高斯泼溅（3DGS）通过光栅化3D基元实现更快渲染。辐射泡沫（RadiantFoam, RF）重新引入射线追踪，通过显式Voronoi图组织辐射，达到与高斯泼溅相当的吞吐量。然而，上述方法在精确网格重建方面仍存在不足。本文提出一种新方法，通过联合学习显式Voronoi图与隐式符号距离场（SDF），利用射线追踪优化场景，并以Eikonal目标正则化。SDF提供度量一致的等值面，引导近表面的Voronoi单元面与其零水平集对齐。所提模型生成的表面更清晰、视角一致，浮点伪影更少，拓扑更优，同时保持辐射保真度和与RadiantFoam相当的训练速度。在多样场景中，该混合隐式-显式框架（命名为SDFoam）显著提升网格重建精度（切比雪夫距离），外观质量（PSNR、SSIM）相当，且不牺牲效率。
原文摘要 · Abstract (English)
Neural radiance fields (NeRF) have driven impressive progress in view synthesis by using ray-traced volumetric rendering. Splatting-based methods such as 3D Gaussian Splatting (3DGS) provide faster rendering by rasterizing 3D primitives. RadiantFoam (RF) brought ray tracing back, achieving throughput comparable to Gaussian Splatting by organizing radiance with an explicit Voronoi Diagram (VD). Yet, all the mentioned methods still struggle with precise mesh reconstruction. We address this gap by jointly learning an explicit VD with an implicit Signed Distance Field (SDF). The scene is optimized via ray tracing and regularized by an Eikonal objective. The SDF introduces metric-consistent isosurfaces, which, in turn, bias near-surface Voronoi cell faces to align with the zero level set. The resulting model produces crisper, view-consistent surfaces with fewer floaters and improved topology, while preserving photometric quality and maintaining training speed on par with RadiantFoam. Across diverse scenes, our hybrid implicit-explicit formulation, which we name SDFoam, substantially improves mesh reconstruction accuracy (Chamfer distance) with comparable appearance (PSNR, SSIM), without sacrificing efficiency.
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