GPGS: Geometric Priors for 3D Gaussian Splatting in Structural Environments

Abstract

Recently, 3D Gaussian Splatting (3DGS) has garnered significant attention for its remarkable capacity to efficiently synthesize novel views with high fidelity. Nevertheless, 3DGS encounters challenges in accurately representing the geometry of real-world scenes. To address this issue, previous methods commonly utilize a depth-normal consistency term on 2D images to regulate the geometry of 3D Gaussians. However, these methods degrade in performance when dealing with low-texture surfaces or limited training views. In contrast, we present GPGS, a novel approach that directly regulates Gaussians in 3D space using Geometric Priors (GP). Given posed LiDAR scans and images, we organize the point clouds into a hierarchical voxel map. Each voxel contains occupancy information and explicitly reveals the internal planar or non-planar structure. We propose a novel divide-and-conquer strategy to separately regulate Gaussians in planar and non-planar voxels. For planar voxels, we design positional and rotational constraints to align Gaussians with the estimated plane. Considering the noisy ranging measurements of complex structures, we use depth-normal consistency to regularize Gaussians in non-planar voxels. Additionally, an occupancy-aware density control strategy is introduced to confine the densification process within occupied voxels, thus reducing artifacts. Extensive experiments on real-world datasets show that our proposed approach outperforms existing state-of-the-art methods in both geometric accuracy and visual quality.