LSADS-Gaussian: Gaussian Splatting for Large-Scale Autonomous Driving Scene Reconstruction

Abstract

The rapid advancement of 3D scene understanding techniques presents a significant opportunity for enhancing autonomous driving simulation systems. As these systems are increasingly required to operate in complex, large-scale, and unbounded real-world environments, efficient and high-fidelity 3D reconstruction of common outdoor scenes has become a critical prerequisite for realistic and extensible autonomous driving simulation. 3D Gaussian Splatting has achieved state-of-the-art performance in novel view synthesis, coupled with real-time rendering efficiency. However, large-scale reconstruction for autonomous driving scenarios faces several challenges as scenes grow in complexity: (1) limited views with insufficient pose diversity, (2) inadequate representation of geometric structural details, and (3) complex lighting conditions involving saturation and shadow variations. To cope with these challenges, we propose LSADS-Gaussian, a novel model for large-scale autonomous driving scene reconstruction. The model consists of a Multimodal Gaussian Network (MGN) module composed of two Gaussian sub-networks, designed to perform Gaussian aggregation and optimization from multi-sensor data, a Geometric Representation Guidance (GRG) module refines and enhances geometric consistency, and a Lighting Enhancement (LE) module introduces learnable illumination coefficients to maintain illumination consistency. Extensive experiments show that LSADS-Gaussian outperforms the state-of-the-art methods.