SpotlessGS: Relightable 3D Gaussian Splatting under Dynamic Illumination for Robotic Perception
Authors: Liang Hong, Jiaxin Wei, Simon Schaefer, Stefan Leutenegger, Jaehyung Jung
Organizations: Mobile Robotics Lab, School of Computation, Information and Technology, Technical University of Munich. · Mobile Robotics Lab, Department of Mechanical and Process Engineering, ETH Zurich.
Robots operating in dark or poorly lit environments rely on onboard lights, which often produce uneven illumination that degrades downstream perception tasks. Prior approaches based on 2D image enhancement lack reliable supervision and fail to preserve multi-view geometric consistency. To address these limitations, we extend Dark Gaussian Splatting (DarkGS) toward a more accurate and flexible relightable 3D reconstruction framework. First, we eliminate the need for explicit light parameter calibration by jointly optimizing lighting parameters within the Gaussian Splatting framework. Second, we introduce a low-frequency illumination model based on spherical harmonics (SH) to capture spatially varying residual and ambient lighting effects. Third, we incorporate an MLP-based Bidirectional Reflectance Distribution Function (BRDF) to model non-Lambertian reflectance. Experiments on synthetic and real-world datasets demonstrate that our method effectively mitigates illumination artifacts while improving rendering quality and quantitative performance over prior approaches. We further validate its benefits for robotic perception through a downstream task.
Figures & tables
Fig. 1 : Robotic platforms and representative onboard images under localized spotlight illumination. Onboard lighting causes over- and under-exposure, producing shadows and saturated highlights that degrade perception. Our method reconstructs a geometrically consistent 3D scene and relights it with uniform illumination, mitigating these artifacts while preserving structure. Robots and onboard images are from [ 1 , 2 , 3 ] .
Fig. 2 : Overview of the SpotlessGS framework.
Fig. 3 : Photo-realistic simulator in Blender for generating synthetic dataset.
Fig. 4 : Qualitative comparison on the synthetic (Uneven, Saturated) and self-collected (Self-col.) dataset.
Dataset
Method
PSNR ↑
SSIM ↑
LPIPS ↓
Synthetic Dataset
Vanilla 3DGS [ 19 ]
6.04
0.391
0.424
DarkGS [ 10 ]
10.66
0.685
0.460
Ours
12.67
0.727
0.381
Self-collected Dataset
Vanilla 3DGS [ 19 ]
7.26
0.333
0.630
DarkGS [ 10 ]
11.09
0.749
0.604
Ours
17.46
0.804
0.513
TABLE I : Quantitative Evaluation on Synthetic and Real-World Datasets.
Fig. 5 : Qualitative comparison on the DarkGS dataset [ 10 ] and real-world dataset in the wild [ 2 , 3 ] .
Fig. 6 : Downstream COLMAP reconstruction comparison. Left: Reconstruction from unevenly illuminated images. Right: Reconstruction from our relit images. Top: sparse point clouds with camera poses. Bottom: enlarged camera trajectories. Colors indicate temporal progression (blue → red). The relit images yield a smoother and physically plausible trajectory with more geometrically consistent point cloud.
Method
Reprojection Error ↓
Observations per Image ↑
Original (Uneven)
1.164 px
480.75
Vanilla 3DGS [ 19 ]
0.733 px
290.76
DarkGS [ 10 ]
0.611 px
881.78
Ours
0.463 px
960.28
TABLE II : Quantitative Comparison on COLMAP reconstruction.
Fig. 7 : Qualitative results of the ablation study on SH-based ambient illumination and the MLP-based BRDF.
Dataset
Method
PSNR ↑
SSIM ↑
LPIPS ↓
Syn. Dataset
DarkGS (Baseline) [ 10 ]
10.66
0.685
0.460
+ SH Ambient Light
12.62
0.724
0.381
+ MLP-based BRDF
10.85
0.721
0.403
+ SH + MLP (Ours)
12.67
0.727
0.381
Self-col. Dataset
DarkGS (Baseline) [ 10 ]
11.09
0.749
0.604
+ SH Ambient Light
15.96
0.768
0.524
TABLE III : Ablation study on the effect of SH-based ambient light and MLP-based BRDF on the synthetic (Syn.) and real-world self-collected (Self-col.) datasets.
Fig. 8 : Effect of Multi-view Rendering
Method
PSNR ↑
SSIM ↑
Tmp. PSNR ↑
Tmp. SSIM ↑
Cycle-GAN [ 9 ]
28.97
0.580
14.92
0.615
Ours
32.05
0.887
15.65
0.664
TABLE IV : Effect of multi-view rendering on synthetic dataset.