TGA-GS: Thermal Geometrically Accurate Gaussian Splatting - Chen Zou; Qingsen Ma; Jia Wang; Rongfeng Lu; Ming Lu; Zhaowei Qu (Applied Sciences)
Novel view synthesis and 3D reconstruction have been extensively studied. Three-dimensional Gaussian Splatting (3DGS) has gained popularity due to its rapid training and real-time rendering capabilities. However, RGB imaging is highly dependent on ideal illumination conditions. In low-light situations such as at night or in the presence of occlusions, RGB images often suffer from blurred contours or even complete failure in imaging, which severely restricts the application of 3DGS in such scenarios. Thermal imaging technology, on the other hand, serves as an effective complement. Thermal images are solely influenced by heat sources and are immune to illumination conditions. This unique property enables them to clearly identify the contour information of objects in low-light environments. Nevertheless, thermal images exhibit significant limitations in presenting texture details due to their sensitivity to temperature variations rather than surface texture features. To capitalize on the strengths of both, we propose thermal geometrically accurate Gaussian Splatting (TGA-GS), a novel Gaussian Splatting model. TGA-GS is designed to leverage RGB and thermal information to generate high-quality meshes in low-light conditions. Meanwhile, given low-resolution thermal images and low-light RGB images as inputs, our method can generate high-resolution thermal and RGB images from novel viewpoints. Moreover, we also provide a real thermal imaging dataset captured with a handheld thermal infrared camera. This not only enriches the information content of the images but also provides a more reliable data basis for subsequent computer vision tasks in low-light scenarios.