Thermal3D-GS: Physics-induced 3D Gaussians for Thermal Infrared Novel-view Synthesis with a Large-Scale Dataset

Thermal infrared imaging has attracted widespread attention in many fields due to the advantages of all-weather imaging and strong penetration. However, existing methods for thermal infrared novel-view synthesis often produce results with coarse details and floating artifacts, primarily caused by physical factors such as atmospheric transmission effects and thermal conduction. These challenges hinder accurate reconstruction of intricate structures and temperature distributions in thermal scenes, limiting the practical utility of previous approaches. To address these limitations, this paper introduces a physics-induced 3D Gaussian splatting method named Thermal3D-GS, the first novel-view synthesis method that relies exclusively on thermal infrared image. Thermal3D-GS begins by modeling atmospheric transmission effects and thermal conduction in three-dimensional media using neural networks. Additionally, considering the sparse features of infrared images, sparse feature priors are designed to improve the reconstruction accuracy of thermal infrared images. Furthermore, to validate the effectiveness of our method, the first large-scale benchmark dataset named Thermal Infrared Novel-view Synthesis Dataset (TI-NSD) is created. This dataset comprises 50 authentic thermal infrared video scenes, covering indoor, outdoor, traffic and UAV(Unmanned Aerial Vehicle) scenarios, with a total of 15,213 frames of thermal infrared image data. In addition, an expanded validation thermal infrared dataset, which includes three high-resolution scenes and five special scenes under varying atmospheric conditions and complex propagation media is constructed to assess generalization performance of the proposed method. Based on this dataset, this paper experimentally verifies the effectiveness of Thermal3D-GS. The results indicate that our method outperforms the baseline method with a 3.19 dB improvement in PSNR and significantly addresses the issues of floaters and indistinct edge features present in the baseline method. The dataset and our code are both publicly available in https://github.com/mzzcdf/Thermal3DGS.