Physics-inspired RGB-T 3D Gaussian Splatting with Multimodal Interaction

RGB-Thermal (RGB-T) 3D reconstruction combines the detail richness of RGB with the all-weather capability of thermal imaging, but faces challenges in jointly balancing both modalities and modeling their physical correlations. Prior RGB-T methods either degrade RGB reconstruction quality or overlook cross-modal physical relationships, leading to artifacts in distant regions and near object boundaries. To address these challenges, we propose PRT-3D, a novel physics-inspired RGB-T 3D Gaussian Splatting (3DGS) reconstruction framework. Unlike previous data-driven methods, PRT-3D draws inspiration from the physical principles governing thermal imaging to design cross-modal interaction networks that enhance reconstruction. Specifically, we introduce a Light Transmission Network (LTN) that, inspired by atmospheric transmission attenuation, leverages the depth consistency of the RGB modality to improve thermal transmission distance estimation. Furthermore, we design a Thermal Conduction Network (TCN) that, inspired by heat diffusion effects, exploits the edge consistency of RGB images to mitigate thermal boundary blurring. Experiments demonstrate that our method significantly improves thermal reconstruction quality over state-of-the-art approaches (e.g., improving thermal PSNR over ThermalGaussian by 3.72 dB on average), while maintaining RGB quality comparable to or surpassing previous best results, and requiring even less GPU memory on a single RTX 3090 GPU.