The field of 3D Gaussian Splatting is rapidly advancing, with a focus on improving novel view synthesis and scene reconstruction. Recent developments have led to the creation of more efficient and effective methods for generating high-quality 3D scenes from incomplete observations. Notably, researchers are exploring the use of geometric priors, continuous level-of-detail techniques, and robust pose-free reconstruction methods to enhance the accuracy and realism of 3D scenes. Additionally, there is a growing interest in integrating 3D Gaussian Splatting with other techniques, such as signed distance functions and learned image priors, to further improve rendering quality and reduce storage overhead. Overall, these advancements have significant implications for applications in computer vision, robotics, and augmented reality. Noteworthy papers include Geometry-Aware Scene Configurations for Novel View Synthesis, which proposes scene-adaptive strategies for efficient representation capacity allocation, and CLoD-GS, which introduces a continuous level-of-detail mechanism for smooth quality scaling. VA-GS is also notable for its view alignment method, which enhances the geometric representation of 3D Gaussians. UniGS and PAGS demonstrate the potential of unified geometry-aware Gaussian Splatting for multimodal rendering and priority-adaptive Gaussian Splatting for dynamic driving scenes, respectively.