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.

翻译：本文提出了一种在开放场景中实现高效且有效的三维表面重建的新方法。现有基于神经辐射场（NeRF）的方法通常因其采用的隐式表示而需要大量的训练和渲染时间。相比之下，三维高斯泼溅（3DGS）使用显式且离散的表示，因此重建的表面由海量的高斯基元构成，这导致了过高的内存消耗以及在稀疏高斯区域表面细节粗糙的问题。为解决这些问题，我们提出了高斯体素核函数（GVKF），它通过核回归在离散的3DGS基础上建立了连续的场景表示。GVKF融合了快速的3DGS光栅化和高效的场景隐式表示，实现了高保真度的开放场景表面重建。在具有挑战性的场景数据集上的实验证明了我们提出的GVKF的效率和有效性，其特点包括高质量的重建结果、实时的渲染速度，以及在存储和训练内存消耗上的显著节省。