DarkVGGT: Seeing Through Darkness Using Thermal Geometry
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, a structured RGB-T adaptation of pretrained VGGT for robust 3D estimation in low-light scenes. DarkVGGT introduces two complementary modules. First, complementary thermal feature factorization processes thermal tokens through content-preserving and high-frequency residual pathways cont...
| Authors | Minseong Kweon, Wenyuan Zhao, Nuo Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 June 2026 |
| Last updated at source | 12 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.