LR-SGS: Robust LiDAR-Reflectance-Guided Salient Gaussian Splatting for Self-Driving Scene Reconstruction

Abstract

Recent 3D Gaussian Splatting (3DGS) methods have demonstrated the feasibility of self-driving scene reconstruction and novel view synthesis. However, most existing methods either rely solely on cameras or use LiDAR only for Gaussian initialization or depth supervision, while the rich scene information contained in point clouds, such as reflectance, and the complementarity between LiDAR and RGB have not been fully exploited, leading to degradation in challenging self-driving scenes, such as those with high ego-motion and complex lighting. To address these issues, we propose a robust and efficient LiDAR-reflectance-guided Salient Gaussian Splatting method (LR-SGS) for self-driving scenes, which introduces a structure-aware Salient Gaussian representation, initialized from geometric and reflectance feature points extracted from LiDAR and refined through a salient transform and improved density control to capture edge and planar structures. Furthermore, we calibrate LiDAR intensity into reflectance and attach it to each Gaussian as a lighting-invariant material channel, jointly aligned with RGB to enforce boundary consistency. Extensive experiments on the Waymo Open Dataset demonstrate that LR-SGS achieves superior reconstruction performance with fewer Gaussians and shorter training time. In particular, on Complex Lighting scenes, our method surpasses OmniRe by 1.18 dB PSNR.

Figures (7)

• Figure 1: Overview of LR-SGS. Given RGB and LiDAR sequences as input, the method produces high-fidelity geometry, reflectance, and RGB renderings (left). Accurate modeling of background and objects enables realistic scene editing, including replacement and deletion (right).
• Figure 2: Method Overview. The initial scene Gaussians comprise Salient Gaussians from LiDAR feature points and Non-Salient Gaussians from SfM points. The scene is represented as a 3DGS scene graph with background, dynamic objects, and sky nodes. After obtaining the rendered Color, Depth, and Reflectance (Refle.) images, we optimize the scene parameters by minimizing a weighted sum of the Color, LiDAR, and Joint losses.
• Figure 3: Our Transform and Split. The dashed line represents the Gaussian shape before executing split. $\uparrow$ and $\downarrow$ denote that the high and low threshold conditions are satisfied, respectively.
• Figure 4: Qualitative Comparison of Novel View Synthesis. (a) shows the Dense Traffic scene. (b) shows the High-Speed scene. (c) and (d) show the scene with Complex Lighting conditions. (e) shows the Static scene. Our method not only achieves high-quality reconstruction of dynamic objects, but also recovers very fine details of the background environment, maintaining consistent and stable reconstructions even under complex lighting conditions.
• Figure 5: Ablation study on Salient Gaussians. Salient Gaussians enable finer reconstruction of structural features in the environment.