In this paper we present a novel method for efficient and effective 3D surface reconstruction in open scenes. Existing Neural Radiance Fields (NeRF) based works typically require extensive training and rendering time due to the adopted implicit representations. In contrast, 3D Gaussian splatting (3DGS) uses an explicit and discrete representation, hence the reconstructed surface is built by the huge number of Gaussian primitives, which leads to excessive memory consumption and rough surface details in sparse Gaussian areas. To address these issues, we propose Gaussian Voxel Kernel Functions (GVKF), which establish a continuous scene representation based on discrete 3DGS through kernel regression. The GVKF integrates fast 3DGS rasterization and highly effective scene implicit representations, achieving high-fidelity open scene surface reconstruction. Experiments on challenging scene datasets demonstrate the efficiency and effectiveness of our proposed GVKF, featuring with high reconstruction quality, real-time rendering speed, significant savings in storage and training memory consumption.
本文提出了一种用于高效3D表面重建的新方法，适用于开放场景。现有基于神经辐射场（NeRF）的工作通常由于采用隐式表示，需较长的训练和渲染时间。相比之下，3D Gaussian Splatting（3DGS）使用显式且离散的表示，因此重建的表面依赖于大量的高斯基元，导致内存消耗过大，并在高斯稀疏区域产生粗糙的表面细节。为了解决这些问题，我们提出了高斯体素核函数（Gaussian Voxel Kernel Functions, GVKF），通过核回归在离散的3DGS基础上建立连续的场景表示。GVKF结合了快速的3DGS光栅化和高效的场景隐式表示，实现了高保真的开放场景表面重建。在具有挑战性的数据集上的实验表明，我们提出的GVKF在重建质量、实时渲染速度、存储及训练内存消耗方面均表现出色。