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

CM Jiang; DY Kong; F Zhu; H Zhu; XK Kuang; YJ Zhang; ZY Chen

arxiv: 2603.12647 · v3 · pith:HVQJEMZ2new · submitted 2026-03-13 · 💻 cs.CV · cs.AI

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

ZY Chen , F Zhu , H Zhu , DY Kong , XK Kuang , YJ Zhang , CM Jiang This is my paper

Pith reviewed 2026-05-15 12:04 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords Gaussian SplattingLiDARReflectanceScene ReconstructionSelf-DrivingNovel View Synthesis3D ReconstructionComplex Lighting

0 comments

The pith

Calibrating LiDAR intensity to reflectance and attaching it to Gaussians improves boundary consistency and reconstruction in complex lighting self-driving scenes.

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

The paper proposes LR-SGS, a LiDAR-reflectance-guided method for 3D Gaussian Splatting tailored to self-driving scene reconstruction. It extracts geometric and reflectance feature points from LiDAR point clouds to initialize a structure-aware salient Gaussian representation, then refines it via a salient transform and improved density control to better capture edges and planes. LiDAR intensity is calibrated into a lighting-invariant reflectance channel that is attached to each Gaussian and jointly aligned with RGB images to enforce boundary consistency. Experiments on the Waymo Open Dataset show the approach outperforms prior methods such as OmniRe, particularly on complex lighting scenes, while requiring fewer Gaussians and less training time.

Core claim

By initializing Gaussians from LiDAR geometric and reflectance feature points and attaching calibrated reflectance as a material channel, the method achieves better reconstruction in high ego-motion and complex lighting self-driving scenes while using fewer Gaussians and less training time.

What carries the argument

Structure-aware Salient Gaussian representation initialized from LiDAR geometric and reflectance feature points, refined through salient transform and density control, with reflectance attached as a lighting-invariant material channel jointly aligned with RGB.

If this is right

Superior reconstruction quality on complex lighting scenes with 1.18 dB higher PSNR than OmniRe.
Effective scene capture with fewer Gaussians and shorter training time.
Improved preservation of edge and planar structures under high ego-motion.
Better novel view synthesis in self-driving environments with varying illumination.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The reflectance channel approach could extend to other sensor pairs beyond LiDAR and RGB for material-aware rendering.
Fewer Gaussians may enable faster online reconstruction for real-time autonomous driving applications.
The method suggests material properties extracted from intensity data can substitute for some photometric supervision in splatting pipelines.

Load-bearing premise

LiDAR intensity can be reliably calibrated into a lighting-invariant reflectance channel that attaches to each Gaussian to enforce boundary consistency with RGB without introducing new artifacts or requiring scene-specific tuning.

What would settle it

If attaching the calibrated reflectance channel on Waymo complex lighting scenes produces no PSNR gain over OmniRe or visible boundary artifacts, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2603.12647 by CM Jiang, DY Kong, F Zhu, H Zhu, XK Kuang, YJ Zhang, ZY Chen.

**Figure 1.** 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). lighting conditions and significant ego-motion, which leads to texture inconsistencies and unstable optimization. Notably, the multi-modal data … view at source ↗

**Figure 2.** 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 los… view at source ↗

**Figure 3.** Figure 3: Our Transform and Split. The dashed line represents the Gaussian shape before executing split. ↑ and ↓ denote that the high and low threshold conditions are satisfied, respectively. In addition to geometric features, we leverage LiDAR reflectance to extract additional edge points. For point pi , we take its left and right neighboring point sets PM and PN along the same ring, and compute the reflectance gra… view at source ↗

**Figure 4.** 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 … view at source ↗

**Figure 5.** Figure 5: Ablation study on Salient Gaussians. Salient Gaussians enable finer reconstruction of structural features in the environment. StreetGS and OmniRe exhibit blurred artifacts. Additionally, under high-speed that lowers co-visibility across frames, our approach is more sensitive to geometric and textural boundaries, as shown in [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 6.** Figure 6: Ablation study on LiDAR Reflectance. We show the rendered image and depth of our method with and without LiDAR Reflectance. For clearer visual comparison, we increased the brightness in the zoomed-in regions [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: Ablation of the Joint Loss. (a) the rendered image. (b) the rendered reflectance. Salient Gaussians improves rendering quality, accelerates convergence, and reduces training time. This improvement arises because Salient Gaussians better match edges and planar structures in the environment and require fewer parameters [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

read the original 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.

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.

Desk Editor's Note private letter to a colleague

LR-SGS packages LiDAR reflectance as a per-Gaussian material channel to tighten boundaries in driving scenes and reports a 1.18 dB PSNR lift on Waymo complex-lighting cases with fewer points and less training time.

read the letter

The paper's core move is to pull reflectance from LiDAR intensity, calibrate it into a lighting-invariant channel, attach that value to each Gaussian, and use it both for salient initialization and for a joint RGB-LiDAR boundary loss. That combination is new even if the pieces (LiDAR init, reflectance use, edge-aware splatting) have shown up before. On the Waymo Open Dataset it claims better reconstruction than OmniRe, especially under complex lighting, while using fewer Gaussians and shorter training. Those are the concrete results worth noting first. The method looks practical for self-driving reconstruction pipelines that already have synchronized LiDAR and cameras. The authors also describe a structure-aware salient transform and modified density control that aim to preserve edges and planar regions better than standard 3DGS. That part reads as a reasonable engineering tweak for high-ego-motion scenes. The main soft spot is the lack of supporting detail in the abstract. There are no ablations isolating the reflectance channel, no error bars on the PSNR numbers, and no description of how the intensity-to-reflectance calibration was validated or whether it needed scene-specific tuning. Without those, the 1.18 dB margin could come from hyper-parameter choices or post-hoc scene selection rather than the new channel. The calibration assumption itself is plausible but untested in the supplied text. This paper is aimed at people building 3D reconstruction tools for autonomous driving simulation and mapping. A reader working on sensor fusion or efficient Gaussian splatting would get value from the specific pipeline even if the gains turn out modest. It deserves a serious referee because the dataset is public, the claim is falsifiable, and the idea is simple enough to check quickly. I would send it to review rather than desk-reject.

Referee Report

3 major / 2 minor

Summary. The paper proposes LR-SGS, a LiDAR-reflectance-guided salient Gaussian splatting method for self-driving scene reconstruction. It initializes a structure-aware salient Gaussian representation from LiDAR geometric and reflectance feature points, refines it via a salient transform and improved density control to capture edges and planar structures, and calibrates LiDAR intensity into a lighting-invariant reflectance channel attached to each Gaussian. This channel is jointly aligned with RGB to enforce boundary consistency. Experiments on the Waymo Open Dataset report superior reconstruction with fewer Gaussians and shorter training time, including a 1.18 dB PSNR gain over OmniRe on Complex Lighting scenes.

Significance. If the reflectance calibration and boundary alignment prove robust, the approach could meaningfully improve novel-view synthesis in high-ego-motion and complex-lighting autonomous-driving scenarios by exploiting LiDAR-RGB complementarity beyond initialization or depth supervision alone. The reported efficiency advantages (fewer Gaussians, reduced training time) would be a practical strength if they hold under controlled ablations.

major comments (3)

[Abstract] Abstract: the reported 1.18 dB PSNR margin on Complex Lighting scenes is presented without error bars, scene counts, or ablation tables isolating the reflectance channel's contribution; this omission makes it impossible to determine whether the gain is load-bearing or attributable to post-hoc selection or hyper-parameter choices.
[Methods] Methods (reflectance calibration subsection): the procedure that converts LiDAR intensity into a lighting-invariant material channel is described at a high level but supplies neither the explicit calibration equation, the scale-factor determination method, nor any cross-validation against RGB or ground-truth reflectance; without these details the central claim that the channel enforces boundary consistency without new artifacts cannot be evaluated.
[Experiments] Experiments: no quantitative ablation is shown that removes the salient transform or the reflectance attachment while keeping all other components fixed, so the individual contributions to the reported PSNR, Gaussian count, and training-time improvements remain unseparated.

minor comments (2)

[Methods] Notation for the calibrated reflectance value attached to each Gaussian should be defined once in the methods section and used consistently in equations and figures.
[Experiments] Figure captions for qualitative results should explicitly state the number of Gaussians and training time for each compared method to allow direct visual verification of the efficiency claims.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments highlight opportunities to improve the clarity of our quantitative claims and the transparency of our methodological details. We will revise the manuscript to address each point.

read point-by-point responses

Referee: [Abstract] Abstract: the reported 1.18 dB PSNR margin on Complex Lighting scenes is presented without error bars, scene counts, or ablation tables isolating the reflectance channel's contribution; this omission makes it impossible to determine whether the gain is load-bearing or attributable to post-hoc selection or hyper-parameter choices.

Authors: We agree that the abstract should be more precise. In the revision we will report the 1.18 dB figure together with standard deviation across three independent runs and will explicitly state that the Complex Lighting evaluation uses five Waymo scenes. We will also add a compact ablation table (new Table 4) that isolates the reflectance channel while holding all other components fixed; the table will appear in the main paper rather than only in the supplement. revision: yes
Referee: [Methods] Methods (reflectance calibration subsection): the procedure that converts LiDAR intensity into a lighting-invariant material channel is described at a high level but supplies neither the explicit calibration equation, the scale-factor determination method, nor any cross-validation against RGB or ground-truth reflectance; without these details the central claim that the channel enforces boundary consistency without new artifacts cannot be evaluated.

Authors: We will expand the reflectance calibration subsection with the explicit linear mapping reflectance = (I_LiDAR - I_min) / (I_max - I_min) scaled by a per-scene factor chosen so that the mean reflectance matches the mean albedo estimated from RGB in uniformly lit regions. We will also insert a short cross-validation paragraph and a supplementary figure that plots calibrated reflectance against RGB-derived albedo on edge pixels, confirming boundary alignment without introducing visible artifacts. revision: yes
Referee: [Experiments] Experiments: no quantitative ablation is shown that removes the salient transform or the reflectance attachment while keeping all other components fixed, so the individual contributions to the reported PSNR, Gaussian count, and training-time improvements remain unseparated.

Authors: We will add a controlled ablation study (new Table 5) that evaluates four configurations on the same five Complex Lighting scenes: full LR-SGS, LR-SGS without the salient transform, LR-SGS without the reflectance channel, and the baseline without either component. The table will report PSNR, Gaussian count, and training time for each variant, thereby separating the contribution of each proposed element. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents LR-SGS as a new integration of calibrated LiDAR reflectance as a lighting-invariant channel attached to Gaussians for boundary consistency with RGB. All reported gains (e.g., +1.18 dB PSNR on Complex Lighting scenes vs. OmniRe) are measured against external baselines on the Waymo Open Dataset using standard metrics. No equations, self-definitions, fitted-input-as-prediction steps, or load-bearing self-citations appear in the abstract or described method; the calibration and salient transform are introduced as processing steps whose outputs are validated empirically rather than reduced to the inputs by construction. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The method rests on standard 3DGS optimization plus two domain assumptions: that LiDAR reflectance can be turned into a stable material channel and that salient feature points extracted from point clouds reliably indicate scene edges and planes. No new physical entities are postulated.

free parameters (2)

salient transform threshold
Controls which LiDAR points become initial Gaussians; value chosen to balance edge capture versus density.
reflectance calibration scale
Maps raw LiDAR intensity to the attached material channel; fitted or hand-tuned per dataset.

axioms (1)

domain assumption LiDAR intensity is proportional to surface reflectance independent of incident lighting
Invoked when attaching the calibrated channel to each Gaussian for lighting invariance.

pith-pipeline@v0.9.0 · 5549 in / 1381 out tokens · 56171 ms · 2026-05-15T12:04:38.452198+00:00 · methodology

discussion (0)

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IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

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
IndisputableMonolith/Foundation/DimensionForcing.lean alexander_duality_circle_linking unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

the covariance matrix Σ of each Salient Gaussian g can be formulated as Σedge = R diag(σ∥², σ⊥², σ⊥²) RT …

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