用热成像增强暗光环境3D重建，不牺牲亮光性能
DarkVGGT: Seeing Through Darkness Using Thermal Geometry without Daylight Tax
- 结合热成像与可见光，通过物理建模提取几何一致的热信号
- 在低光照下深度和相机位姿估计精度显著优于现有方法
- 适合自动驾驶、夜视监控等暗光场景的3D感知应用
近期的前馈式3D重建方法在从图像流中高效端到端估计场景几何方面表现出色。然而，其对可见光外观的依赖使其在黑暗和低能见度环境中表现脆弱，因为RGB线索严重退化，几何证据变得模糊。为解决此问题，我们提出DarkVGGT，一种基于RGB-T的前馈几何框架，利用物理感知的热建模实现低光照场景下的鲁棒3D估计。DarkVGGT引入两个互补模块：首先，受物理启发的热因子分解提取以发射为主、几何一致的热信号，同时分离可能引入几何歧义的稀疏反射残差；其次，几何共享热路由从热特有模式中分离出模态不变的几何结构，选择性地将可靠性感知的结构引导注入RGB流。这两个组件共同实现退化RGB条件下的精准热引导几何估计，同时在光照充足环境下基本保持性能。在低能见度RGB-T基准测试上的实验表明，其在深度和相机姿态估计上均持续优于现有前馈几何基线。
原文摘要 · Abstract (English)
Recent feed-forward 3D reconstruction methods have demonstrated strong performance and flexibility in efficient end-to-end scene geometry estimation from image streams. However, their reliance on visible-light appearance makes them vulnerable in dark and low-visibility environments, where RGB cues are severely degraded and geometric evidence becomes ambiguous. To address this challenge, we propose DarkVGGT, an RGB-T feed-forward geometry framework that uses physics-aware thermal modeling for robust 3D estimation in low-light scenes. DarkVGGT introduces two complementary modules. First, physics-inspired thermal factorization extracts emissive-dominant, geometry-consistent thermal cues while isolating sparse reflective residuals that may introduce geometric ambiguity. Second, geometry-shared thermal routing isolates modality-invariant geometric structures from thermal-specific patterns, selectively injecting reliability-aware structural guidance into the RGB stream. Together, these components enable accurate thermal-informed geometry estimation under degraded RGB conditions while largely preserving performance in well-lit environments. Experiments on low-visibility RGB-T benchmarks demonstrate consistent improvements in both depth and camera pose estimation over existing feed-forward geometry baselines.
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