NTR-Gaussian: Nighttime Dynamic Thermal Reconstruction with 4D Gaussian Splatting Based on Thermodynamics
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
Thermal infrared imaging offers the advantage of all-weather capability,
enabling non-intrusive measurement of an object's surface temperature.
Consequently, thermal infrared images are employed to reconstruct 3D models
that accurately reflect the temperature distribution of a scene, aiding in
applications such as building monitoring and energy management. However,
existing approaches predominantly focus on static 3D reconstruction for a
single time period, overlooking the impact of environmental factors on thermal
radiation and failing to predict or analyze temperature variations over time.
To address these challenges, we propose the NTR-Gaussian method, which treats
temperature as a form of thermal radiation, incorporating elements like
convective heat transfer and radiative heat dissipation. Our approach utilizes
neural networks to predict thermodynamic parameters such as emissivity,
convective heat transfer coefficient, and heat capacity. By integrating these
predictions, we can accurately forecast thermal temperatures at various times
throughout a nighttime scene. Furthermore, we introduce a dynamic dataset
specifically for nighttime thermal imagery. Extensive experiments and
evaluations demonstrate that NTR-Gaussian significantly outperforms comparison
methods in thermal reconstruction, achieving a predicted temperature error
within 1 degree Celsius.
