# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 1 Introduction  
The field of scene reconstruction and novel view synthesis based on visible light has achieved remarkable progress, with applications spanning various domains. These applications include creating immersive experiences for users in virtual reality (VR), aiding vehicles in environmental perception and path planning for autonomous driving, assisting robots in better understanding and interacting with their environments, supporting architectural design and monitoring in the construction industry, digital twinning, and even playing crucial roles in specialized fields such as materials research and electronic component inspection. The advent of Neural Radiance Fields (NeRFs) [1] brings new perspectives and methods to traditional 3D reconstruction and novel view synthesis. NeRFs utilize multi-layerperceptrons (MLPs) to simulate scene radiance fields through differentiable volume rendering, enabling the learning of 3D scene representations from a set of 2D images and achieving realistic view synthesis. However, NeRFs suffer from slow training and rendering speeds, which to some extent limit their applications in scenarios demanding real-time performance. This limitation spurs the development of 3D Gaussian Splatting (3 DGS) [2], which uniquely transforms the existing technical framework. By representing scenes using a multitude of 3D Gaussians, 3 DGS can achieve real-time rendering while maintaining visual fidelity comparable to NeRFs, leading to a significant leap in the efficiency of 3D reconstruction and novel view synthesis. Nevertheless, most current 3 DGS methods primarily process RGB image data under ideal lighting conditions. In real-world scenarios such as nighttime, low-light indoor environments, or situations with significant occlusions, RGB images face numerous challenges. Due to the photon-starved effect [3], performance significantly deteriorates under low-light conditions, leading to increased image noise, reduced contrast, blurred object contours, and sometimes the inability to acquire sufficient effective image information, greatly limiting the effectiveness and reconstruction accuracy of 3 DGS technology in these special scenarios. At the same time, thermal imaging technology, as a crucial imaging modality, offers irreplaceable advantages. Thermal images primarily reflect the temperature distribution on the surface of objects, capturing mid-wave infrared (MWIR, 3-5 \(\upmu\)m) and long-wave infrared (LWIR, 8-14 \(\upmu\)m) radiation emitted by objects based on their temperature and emissivity [4]. Unlike passive RGB sensors, thermal sensors provide illumination-invariant detection capabilities, enabling clear identification of object contours and approximate shapes even in complete darkness, smoke/fog [5], and extreme weather conditions [6], facilitating robust scene understanding. Leveraging this capability, thermal imaging can distinctly identify object contours and approximate shapes in low-light environments, providing crucial complementary information for scene understanding. With these advantages, thermal imaging technology finds wide applications in various fields. In military and defense [7; 8; 9; 10], it aids in target detection and recognition; in post-disaster rescue scenarios [11; 12; 13], it helps rescue teams quickly locate survivors; in environmental protection [14; 15; 16; 17; 18], it detects the pollution status of water and space; in the construction industry [19; 20; 21; 22; 23; 24; 25], it detects thermal defects in building structures; in agriculture [18; 26; 27; 28; 29; 30; 31; 32; 33], it helps monitor crop growth conditions; in industry [6; 34], it is used for equipment fault diagnosis; in the medical field [35; 36; 37], it assists in disease detection and diagnosis; and in autonomous driving [38; 39], it provides additional perceptual information for vehicles under adverse lighting conditions.