# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 1 Introduction
Additionally, thermal imaging technology also plays a significant role in computer vision tasks such as object detection and tracking [40; 41; 42], recognition and segmentation [43; 44; 45], motion estimation [46], and SLAM [47]. Despite these benefits, thermal imaging poses inherent challenges for 3D reconstruction: (1) lower spatial resolution (usually \(640\times 480\)) compared to RGB sensors [48], (2) lack of photometric texture details due to thermal diffusion effects [49; 50; 51], and (3) spectral mismatch between thermal radiation peaks (8-14 \(\upmu\)m) and visible light (380-750 nm) complicating the fusion of cross-modal data [52]. In recent years, although some thermal image methods based on NeRFs have been proposed [53; 54; 55; 56; 57; 58; 59; 60], attempting to leverage strong modeling capabilities of NeRFs to handle thermal image data, the inherent slow rendering speed and implicit scene representation of NeRFs still constrain the practical effectiveness of these methods. With the widespread application of 3 DGS methods, there have also been emerging thermal image methods based on Gaussian Splatting (GS) models [61; 62; 63; 64]. However, most of these methods mainly focus on novel view synthesis under normal lighting conditions and fail to fully exploit the potential of thermal imaging in low-light environments. Given the current research status, our study, based on an in-depth examination and analysis of existing technologies, introduces an innovative method called thermal geometrically accurate Gaussian Splatting (TGA-GS) based on the GS model. TGA-GS can simultaneously process thermal and RGB data from thermal sensors, leveraging the complementary information from both types of data to achieve the goal of reconstructing high-quality scenes from low-light environments effectively. Notably, our method possesses unique advantages: even when provided with low-resolution thermal and RGB images under low-light conditions, it can generate high-resolution thermal and RGB images, offering a high-quality data foundation for subsequent image analysis and processing. Furthermore, to better support and validate our research, we construct an innovative dataset. This dataset includes RGB and thermal images captured in low-light environments, providing rich and valuable data resources for 3D reconstruction and novel view synthesis research. In summary, the main contributions of our study are as follows:  
* We propose the TGA-GS method, focusing on 3D thermal model reconstruction in low-light conditions. Extensive experimental results demonstrate that TGA-GS outperforms existing relevant methods in multiple evaluation metrics, showcasing its outstanding performance in 3D reconstruction under low-light environments. * Our method exhibits robust novel view synthesis capabilities, generating high-resolution images from low-resolution inputs, including high-resolution thermal and RGB images, effectively enhancing image quality and information richness. * We create a novel dataset that provides essential data support for 3D reconstruction and novel view synthesis research. This dataset comprises RGB and thermal images captured in low-light environments, offering a wealth of samples for studying image features and reconstruction algorithms under low lighting conditions and facilitating further advancements in related fields. The rest of this paper is organized as follows: Section 2 reviews related work on thermal imaging and 3 DGS. Section 3 provides a detailed introduction to the theoretical derivation and specific implementation of the proposed method, mainly including multimodal calibration, training phase, and loss function construction. Section 4 presents experimental results comparing TGA-GS with state-of-the-art methods on thermal images, RGB images, and 3D reconstruction. Finally, in Section 5, we summarize the work in this paper and look ahead to future work.