# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 2 Related Work  
### Thermal Imaging  
Thermal imaging has long been an important complement to visible light sensors, based on [PERSON]'s blackbody radiation law [65] and the Stefan-Boltzmann equation [66], fundamentally overcoming the limitations of visible light sensors that rely on environmental lighting. Due to the technological breakthrough of uncooled microbolometer arrays [67], this technology has gradually expanded from the early military target recognition field to civilian scenarios [6]. Its unique physical perception mechanism enables the thermal imaging system to operate stably in complete darkness, through smoke/fog [5] and other obstructions and under extreme weather conditions.  
This feature gives thermal imaging a unique advantage in areas such as security and surveillance, preventive maintenance, building inspections [68], monitoring rock formations [69], and more. Due to its non-contact nature, thermal imaging technology has further expanded its applications in medical diagnostics, agricultural monitoring, and archaeological research.  
The hardware design of thermal imaging systems poses inherent challenges; the high cost of germanium lenses [70] and limitations in the detector unit size result in significantly lower spatial resolution of thermal sensors compared to visible light sensors. Additionally, thermal images are susceptible to the ghosting effect, where the environmental thermal radiation reflected or scattered from the object's surface can blur the actual temperature distribution, leading to texture loss and reduced contrast [71]. To overcome these limitations, some studies propose thermal imaging super-resolution [72], while others propose multimodal fusion solutions. For instance, [PERSON] et al. [25] and [PERSON] et al. [20] combine RGB images with thermal images using photogrammetric techniques to build high-precision 3D models, albeit relying on dense RGB-thermal image pairs for acquisition. [PERSON] et al. [73] attempt direct 3D reconstruction using thermal images but find it only suitable for high-contrast scenes and challenging to generalize to complex environments.  
In the field of sensor fusion, early methods often rely on lidar or depth cameras to provide geometric information, projecting thermal data onto 3D models, but these methods require cumbersome manual calibration and are susceptible to errors in depth measurements. In recent years, advancements in deep learning have propelled multimodal fusion. [PERSON] et al. [74] enhance thermal image features through RGB image augmentation to improve depth perception, while [PERSON] et al. [75] fuse inertial measurement unit (IMU) data to optimize attitude estimation. X-NeRF [53] is the first to extend Neural Radiance Fields (NeRFs) to the infrared spectrum, achieving scene modeling through joint learning of RGB-thermal image cross-spectral representations. Subsequent works expand into the thermal NeRF [54; 55; 56; 57; 58; 59; 60] and thermal 3 DGS [61; 62; 63; 64] domains. However, current thermal GS methods focus on normal lighting conditions and fail to utilize the advantages of thermal imaging under low-lighting conditions and cannot achieve super-resolution output of thermal images.