An Efficient Geometric Points-Guided Generalized Exponential Splatting

This paper presents a novel Generalized Exponential Splatting framework for scene reconstruction. Unlike neural radiance fields (NeRF), 3D Gaussian Splatting (3DGS) offers rapid rendering and high fidelity. However, existing 3DGS frameworks integrating RGB-D SLAM face challenges in system efficiency and aliasing artifacts. Inspired by geometric features, we first categorize the space into three regions—edge, planar, and non-planar—based on their information richness. The edge extraction is realized through an energy function, while planar and non-planar regions are segmented via a fast segmentation algorithm proposed in this paper. Secondly, a sparseness function is established to screen key points within the these regions to build a geometric points-guided Generalized Exponential Splatting model (GP-GES), strategically placing primitives to reduce redundancy. Finally, by integrating a 3D low-pass filter, a smooth GP-GES model is further built, ensuring that the frequency of the primitives adheres to the Nyquist-Shannon theorem, to mitigate high-frequency artifacts. Extensive experiments show that our method reduces the map size by 3.7× to 6× without sacrificing rendering quality, thereby accelerating the computational efficiency of the system. In addition, the segmentation algorithm has a fast detection speed, with a frame rate exceeding 90Hz.