DarkVGGT: Seeing Through Darkness Using Thermal Geometry without Daylight Tax

• URL: http://arxiv.org/abs/2606.11326v1
• Date: Tue, 09 Jun 2026 18:08:51 GMT
• Title: DarkVGGT: Seeing Through Darkness Using Thermal Geometry without Daylight Tax
• Abstract summary: We propose an RGB-T feed-forward geometry framework that uses physics-aware thermal modeling for robust 3D estimation in low-light scenes.<n>DarkVGGT extracts emissive-dominant, geometry-consistent thermal cues while isolating sparse reflective residuals.<n>Experiments on low-visibility RGB-T benchmarks demonstrate consistent improvements in both depth and camera pose estimation over existing feed-forward geometry baselines.
• Score: 30.354740943798692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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