让3D场景重建在强光下依然准确，解决光照变化导致的失真问题。
Taming the Light: Illumination-Invariant Semantic 3DGS-SLAM
- 用内在外观归一化模块分离光照与物体本色，保持颜色稳定
- 极端曝光时自动启用动态辐射平衡损失，修复画面畸变
- 适合自动驾驶、机器人等光照复杂场景下的高精度定位
极端光照会严重降低3D地图重建和语义分割的准确性，尤其影响紧耦合系统。为此，我们提出一种新型语义SLAM框架，包含两项设计：首先，固有外观归一化（IAN）模块主动解耦场景的固有属性（如反照率）与瞬时光照，通过学习标准化的、光照不变的外观模型，为每个高斯原语赋予稳定一致的颜色表示。其次，动态辐射平衡损失（DRB-Loss）在图像曝光不良时才激活，直接作用于辐射场以引导定向优化，避免极端光照下的误差累积，同时不影响正常条件下的性能。IAN的主动不变性与DRB-Loss的被动修正协同作用，显著提升系统鲁棒性。在公开数据集上的评估表明，该方法在相机跟踪、地图质量、语义与几何精度方面均达到当前最优水平。
原文摘要 · Abstract (English)
Extreme exposure degrades both the 3D map reconstruction and semantic segmentation accuracy, which is particularly detrimental to tightly-coupled systems. To achieve illumination invariance, we propose a novel semantic SLAM framework with two designs. First, the Intrinsic Appearance Normalization (IAN) module proactively disentangles the scene's intrinsic properties, such as albedo, from transient lighting. By learning a standardized, illumination-invariant appearance model, it assigns a stable and consistent color representation to each Gaussian primitive. Second, the Dynamic Radiance Balancing Loss (DRB-Loss) reactively handles frames with extreme exposure. It activates only when an image's exposure is poor, operating directly on the radiance field to guide targeted optimization. This prevents error accumulation from extreme lighting without compromising performance under normal conditions. The synergy between IAN's proactive invariance and DRB-Loss's reactive correction endows our system with unprecedented robustness. Evaluations on public datasets demonstrate state-of-the-art performance in camera tracking, map quality, and semantic and geometric accuracy.
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