C3DGS: Compressing 3D Gaussian Model for Surface Reconstruction of Large-Scale Scenes Based on Multiview UAV Images
Jiating Qian, Yiming Yan, Fengjiao Gao, Baoyu Ge, Maosheng Wei, Boyi Shangguan, Guangjun He
Abstract
Methods based on 3D Gaussian Splatting (3DGS) for surface reconstruction face challenges when applied to large-scale scenes captured by UAV. Because the number of 3D Gaussians increases dramatically, leading to significant computational requirement and limiting the fineness of surface reconstruction. To address this challenge, we propose C3DGS that compresses 3D Gaussian model and ensures the quality of surface reconstruction of large-scale scenes in the face of heavy computational costs. Our method quantifies the contribution of 3D Gaussians to the surface reconstruction and prunes redundant 3D Gaussians to reduce the computational requirement of the model. In addition, pruning 3D Gaussians inevitably incurs loss, and in order to guarantee as many details as possible in the surface reconstruction of a complex scene, we use a ray tracing volume rendering method that can better evaluate the opacity of 3D Gaussians. Furthermore, we introduce two regularization terms to enhance the geometric consistency of multiple views, thus improving the realism of surface reconstruction. Experiments show that our method outperforms other 3DGS-based surface reconstruction methods when facing large-scale scenes.