LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping

• URL: http://arxiv.org/abs/2606.20424v1
• Date: Thu, 18 Jun 2026 16:06:52 GMT
• Title: LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping
• Abstract summary: We present LIT-GS, a LiDAR-inertial-thermal Gaussian Splatting framework.<n>We exploit LIV visual map points as confidence-aware cross-modal anchors to establish reliable thermal-LiDAR associations.<n>We introduce a LiDAR-plane-regularized differentiable splatting objective that constrains rendered 3D points to align with locally observed planes.
• Score: 10.353975244787629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gaussian Splatting has enabled real-time neural rendering, yet existing LiDAR-inertial-visual (LIV) Gaussian mapping pipelines remain fragile under illumination changes and texture-deficient scenes due to their reliance on RGB photometric cues. We present LIT-GS, a LiDAR-inertial-thermal Gaussian Splatting framework that injects LiDAR-derived plane geometry as an explicit constraint in both pose/structure refinement and Gaussian optimization. Specifically, we exploit LIV visual map points as confidence-aware cross-modal anchors to establish reliable thermal-LiDAR associations, and incorporate weighted LiDAR point-to-plane residuals into bundle adjustment to jointly refine camera poses and 3D points under weak thermal supervision. Building on the refined structure, we further introduce a LiDAR-plane-regularized differentiable splatting objective that constrains rendered 3D points to align with locally observed planes, mitigating surface thickening and structural drift in low-contrast thermal imagery. Experiments on proprietary sequences and public datasets demonstrate that LIT-GS consistently improves geometric accuracy and rendering quality over state-of-the-art LIV-based Gaussian Splatting baselines, particularly in challenging lighting conditions.

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