GSurf：通过对不透明高斯点进行体素化学习有符号距离场以实现高质量3D重建
GSurf: Learning Signed Distance Fields from Splatting Opaque Gaussians for High-quality 3D Reconstruction
AI总结：
本文针对多视图图像高保真表面重建问题，提出将有符号距离场集成到体素化管道的框架，利用SDF连续性规范高斯基元，填补空洞、抑制噪声，借助体素化效率实现快速收敛，能以更少基元生成高质量表面。
摘要：
从多视图图像进行高保真表面重建是3D计算机视觉中的一个核心问题。虽然像有符号距离场（SDF）这样的神经隐式表面提供了平滑的几何形状，但它们常常受体绘制计算强度的限制。相反，3D高斯体素化（3DGS）提供了快速训练，但缺乏几何连续性，常常导致表面碎片化。本文提出了一个新颖的框架，将有符号距离场直接集成到体素化管道中。通过利用SDF的连续性来规范高斯基元，我们的方法有效地填补了几何空洞并抑制了稀疏点云中固有的噪声。与依赖大量体素采样的混合方法不同，我们的方法利用体素化的效率实现更快的收敛。广泛的评估表明，我们的方法用显著更少的基元生成了高质量的表面，为室内和室外环境提供了更紧凑和高效的表示。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
