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arxiv: 2606.20424 · v1 · pith:ROMJBGBEnew · submitted 2026-06-18 · 💻 cs.RO

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

Shikuan Shi , Chunran Zheng , Jiaming Xu , Tianyong Ye , Tao Yu , Yukang Cui This is my paper

Pith reviewed 2026-06-26 17:25 UTC · model grok-4.3

classification 💻 cs.RO
keywords Gaussian SplattingLiDAR-Inertial-Thermalillumination-robust mappingcross-modal anchorsplane constraintsbundle adjustmentthermal imagery
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The pith

LiDAR plane constraints and visual anchors enable Gaussian Splatting to maintain accuracy when lighting changes degrade RGB cues.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces LIT-GS, a Gaussian Splatting pipeline that fuses LiDAR, inertial, and thermal sensors to overcome the fragility of standard LiDAR-inertial-visual methods in illumination changes and texture-poor scenes. It links thermal images to LiDAR geometry by treating visual map points as anchors, then enforces LiDAR plane residuals during bundle adjustment and adds a plane-regularized term to the splatting loss. A sympathetic reader would care because reliable real-time 3D mapping is needed for robots and vehicles that must operate across day-night cycles or low-contrast environments where color cameras lose reliability.

Core claim

LIT-GS exploits LIV visual map points as confidence-aware cross-modal anchors to establish thermal-LiDAR associations, incorporates weighted LiDAR point-to-plane residuals into bundle adjustment to jointly refine poses and points under weak thermal supervision, and applies a LiDAR-plane-regularized differentiable splatting objective that constrains rendered points to observed planes, thereby mitigating surface thickening and structural drift while improving geometric accuracy and rendering quality over LIV-based baselines in challenging lighting.

What carries the argument

LiDAR-plane-regularized differentiable splatting objective that constrains rendered 3D points to align with locally observed planes, carrying the structural stability argument under thermal supervision.

Load-bearing premise

LIV visual map points can serve as reliable confidence-aware cross-modal anchors to establish thermal-LiDAR associations under weak thermal supervision.

What would settle it

A low-light sequence where removing the LiDAR point-to-plane residuals or the visual-anchor thermal associations produces equal or higher geometric error than the LIV Gaussian Splatting baseline.

Figures

Figures reproduced from arXiv: 2606.20424 by Chunran Zheng, Jiaming Xu, Shikuan Shi, Tao Yu, Tianyong Ye, Yukang Cui.

Figure 1
Figure 1. Figure 1: Hardware setup of LIT-GS. (a1) thermal observations of the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System overview of LIT-GS. • We propose LIT-GS, a LiDAR-inertial-thermal Gaus￾sian Splatting framework that couples LiDAR plane constraints into both pose/structure refinement and Gaussian optimization, improving metric consistency under illumination changes and texture-deficient scenes. • To stabilize thermal-LiDAR alignment, we develop a confidence-aware cross-modal anchoring and refinement scheme that u… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of renderings on private datasets. Data collection times (left to right) are 12:00 p.m., 10:00 p.m., 2:00 p.m., 7:00 p.m., and 6:00 a.m. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of renderings on public datasets. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Omni-view ablation experiment demonstration. From the perspectives of front, back, left, and right, the refined perspectives (a1-a4) are compared [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper introduces LIT-GS, a LiDAR-inertial-thermal Gaussian Splatting framework for illumination-robust mapping. It leverages LIV visual map points as confidence-aware anchors to link thermal and LiDAR data, incorporates weighted LiDAR point-to-plane residuals into bundle adjustment for pose and structure refinement under weak thermal supervision, and proposes a LiDAR-plane-regularized differentiable splatting objective to constrain rendered points to observed planes. Experiments on proprietary sequences and public datasets show consistent improvements in geometric accuracy and rendering quality over LIV-based baselines, especially in challenging lighting.

Significance. If the results hold, the work offers a meaningful advance in robust neural mapping by integrating thermal imagery with explicit LiDAR geometric constraints, addressing limitations of RGB-dependent methods in low-light or textureless scenes. The plane-regularization in splatting is a concrete technical contribution that could reduce structural drift. Credit is due for evaluating on both proprietary and public datasets, providing a basis for reproducibility.

major comments (1)
  1. [Abstract] Abstract: The central construction relies on 'LIV visual map points as confidence-aware cross-modal anchors' to establish thermal-LiDAR associations under weak thermal supervision. However, the abstract states that existing LIV pipelines are fragile under illumination changes due to RGB photometric cues. This is load-bearing for the claimed gains; if the anchors degrade in the target regime, the reliability of the associations and attribution of improvements to the new thermal-LiDAR components cannot be taken as given. The manuscript must provide quantitative evidence (e.g., anchor survival rates or an ablation removing visual anchors) in the experiments section showing that the mechanism remains effective precisely where RGB fails.
minor comments (1)
  1. Define all acronyms (LIV, LIT-GS, etc.) at first use in the main text and ensure consistent notation for residuals and regularization terms across sections.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We appreciate the request for quantitative evidence on the robustness of the cross-modal anchors under illumination changes and address the point below.

read point-by-point responses

  1. Referee: The central construction relies on 'LIV visual map points as confidence-aware cross-modal anchors' to establish thermal-LiDAR associations under weak thermal supervision. However, the abstract states that existing LIV pipelines are fragile under illumination changes due to RGB photometric cues. This is load-bearing for the claimed gains; if the anchors degrade in the target regime, the reliability of the associations and attribution of improvements to the new thermal-LiDAR components cannot be taken as given. The manuscript must provide quantitative evidence (e.g., anchor survival rates or an ablation removing visual anchors) in the experiments section showing that the mechanism remains effective precisely where RGB fails.

    Authors: We agree that explicit quantitative validation of anchor reliability in low-illumination regimes is necessary to support the attribution of gains. Our current experiments demonstrate consistent improvements in geometric accuracy and rendering on challenging lighting sequences relative to LIV baselines, which indirectly supports that the confidence-aware anchors remain usable when combined with thermal and LiDAR constraints. To directly address the concern, we will add anchor survival rates (computed from the LIV confidence scores) and an ablation removing the visual anchors to the experiments section in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation; claims rest on experimental comparison

full rationale

The abstract and method description present a pipeline that incorporates LIV visual map points as anchors and adds LiDAR-plane constraints, but no equations or fitted quantities are shown reducing to each other by construction. Claims of improvement are supported by experiments on datasets rather than internal re-derivation or self-citation chains that bear the central result. The noted fragility of visual points under illumination is an assumption about input reliability, not a definitional loop in the derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that LiDAR planes remain observable and that thermal-LiDAR associations can be formed reliably.

pith-pipeline@v0.9.1-grok · 5736 in / 1053 out tokens · 19527 ms · 2026-06-26T17:25:13.043594+00:00 · methodology

discussion (0)

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Reference graph

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