Gaussian Splatting (GS) methods, including 3DGS and 2DGS, have demonstrated significant effectiveness in real-time novel view synthesis (NVS), establishing themselves as a key technology in the field of computer graphics. However, GS-based methods still face challenges in rendering high-quality image details. Even when utilizing advanced frameworks, their outputs may display significant rendering artifacts when relying solely on a few input views, such as noise and blurriness. A reasonable approach is to conduct post-processing in order to restore clear details. Therefore, we propose GaussianEnhancer, a general GS-agnostic post-processor that employs a degradation-driven view blending method to improve the rendering quality of GS models. Specifically, we design a degradation modeling method tailored to the GS style and construct a large-scale training dataset to effectively simulate the native rendering artifacts of GS, enabling efficient training. In addition, we present a spatial information fusion framework that includes view fusion and depth modulation modules. This framework successfully integrates related images and leverages depth information from the target image to enhance rendering details. Through our GaussianEnhancer, we effectively eliminate the rendering artifacts of GS models and generate highly realistic image details. Based on GaussianEnhancer, we introduce GaussianEnhancer++, which features an enhanced GS-style degradation simulator, leading to improved enhancement quality. Furthermore, GaussianEnhancer++ can generate ultra-high-resolution outputs from noisy low resolution GS rendered images by leveraging the symmetry between high-resolution and low resolution images. Extensive experiments demonstrate the excellent restoration ability of GaussianEnhancer++ on various novel view synthesis benchmarks.

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.

Under extremely low-light conditions, novel view synthesis (NVS) faces severe degradation in terms of geometry, color consistency, and radiometric stability. Standard 3D Gaussian Splatting (3DGS) pipelines fail when applied directly to underexposed inputs, as independent enhancement across views causes illumination inconsistencies and geometric distortion. To address this, we present DTGS, a unified framework that tightly couples Retinex-inspired illumination decomposition with thermal-guided 3D Gaussian Splatting for illumination-invariant reconstruction. Unlike prior approaches that treat enhancement as a pre-processing step, DTGS performs joint optimization across enhancement, geometry, and thermal supervision through a cyclic enhancement-reconstruction mechanism. A thermal supervisory branch stabilizes both color restoration and geometry learning by dynamically balancing enhancement, structural, and thermal losses. Moreover, a Retinex-based decomposition module embedded within the 3DGS loop provides physically interpretable reflectance-illumination separation, ensuring consistent color and texture across viewpoints. To evaluate our method, we construct RGBT-LOW, a new multi-view low-light thermal dataset capturing severe illumination degradation. Extensive experiments show that DTGS significantly outperforms existing low-light enhancement and 3D reconstruction baselines, achieving superior radiometric consistency, geometric fidelity, and color stability under extreme illumination.