用SDF优化3D高斯点云，让重建更完整高效
GSurf: Learning Signed Distance Fields from Splatting Opaque Gaussians for High-quality 3D Reconstruction
- 将SDF融入高斯溅射流程，利用连续性修复几何空洞
- 仅用少量高斯点即实现高质量表面重建
- 适合追求高效高保真3D重建的开发者
从多视角图像进行高保真表面重建是3D计算机视觉的核心问题。虽然神经隐式表面（如SDF）能提供平滑几何结构，但常受限于体渲染的计算开销。相反，3D高斯溅射（3DGS）虽训练快速，却缺乏几何连续性，常导致表面破碎。本文提出一种新框架，将显式符号距离场（SDF）直接集成到溅射流程中。通过利用SDF的连续性对高斯基元进行正则化，方法有效填补几何空洞并抑制稀疏点云中的噪声。与依赖密集体素采样的混合方法不同，本方法借助溅射效率实现更快收敛。大量实验表明，该方法在室内与室外环境中均以更少的高斯基元生成高质量表面，提供更紧凑高效的表示。
原文摘要 · Abstract (English)
High-fidelity surface reconstruction from multi-view images is a core problem in 3D computer vision. While neural implicit surfaces like SDFs offer smooth geometry, they are often bottlenecked by the computational intensity of volume rendering. Conversely, 3D Gaussian Splatting (3DGS) provides rapid training but lacks geometry continuity, often leading to fragmented surfaces. This paper presents a novel framework that integrates Signed Distance Fields directly into the splatting pipeline. By leveraging the continuous nature of SDFs to regularize Gaussian primitives, our method effectively fills geometric holes and suppresses noise inherent in sparse point clouds. Unlike hybrid approaches that rely on heavy volumetric sampling, our approach utilizes the efficiency of splatting to achieve faster convergence. Extensive evaluations demonstrate that our method produces high-quality surfaces with significantly fewer primitives, offering a more compact and efficient representation for both indoor and outdoor environments.
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