TI-3DGS: 3D Thermal Reconstruction Via Thermal Imaging-Guided 3D Gaussian Splatting

Abstract

Thermal imaging, with its all-weather capabilities and strong penetration, enables 3D reconstruction in low- light and adverse conditions. In this paper, we investigate RGB-independent pure 3D thermal reconstruction, aiming to overcome the challenges of 3D reconstruction in extreme environments where RGB images are unavailable. However, directly applying visible-light 3D reconstruction methods to thermal images often leads to severe artifacts due to two key challenges: (i) thermal images lack rich textures, hindering detail reconstruction, and (ii) heat conduction causes intensity diffusion, resulting in blurred edges. To address these issues, we propose TI-3DGS, a novel 3D Gaussian Splatting framework guided by thermal imaging. We introduce a Thermal Imaging Field (TIF) to model radiance in thermal domains and a Thermal Attenuation-aware Density Control (TADC) strategy to densify sparse point clouds from low-texture thermal inputs. Additionally, we incorporate an edge-enhancement constraint to mitigate blur from heat diffusion. Extensive experiments on the TI-NSD dataset, covering indoor and outdoor scenarios, show that our TI-3DGS achieves state-of-the-art performance, effectively overcoming texture sparsity and edge degradation in thermal reconstruction.