Thermal3R: Physics-Induced Feed-forward Framework for Thermal 3D Reconstruction

Thermal infrared (TIR) imaging enables reliable all-weather perception, yet generalizable 3D reconstruction from TIR remains underdeveloped. Existing feed-forward multi-view models designed for the visible spectrum fail in the thermal domain due to three physical hurdles: depth stretching from nonlinear emission, texture loss from heat conduction, and radiative distortion from atmospheric attenuation. To foster research in thermal 3D reconstruction, we introduce Largethermal3D, a comprehensive and fully-annotated benchmark for thermal 3D reconstruction, and Thermal3R, a physics-guided framework integrating radiation, conduction, and transmission priors. Specifically, we propose: (i) Physics-Aware Radiation Sensitivity Attention Mechanism (PAM) to handle nonlinear sensor response; (ii) Thermal Gradient Consistency (TGC) to sharpen geometric boundaries attenuated by heat conduction; and (iii) Attenuation-Aware Calibration (AAC) to correct atmospheric biases. Experiments show that Thermal3R achieves competitive performance, outperforming fine-tuned RGB baselines across all tasks. This underscores the superiority of physical priors over simple data-driven fine-tuning