The field of 3D reconstruction and rendering is experiencing significant advancements, driven by innovative approaches to Gaussian Splatting and related techniques. A key direction is the elimination of densification steps, which traditionally slow down the reconstruction process and lead to suboptimal renderings. Novel methods are being proposed to enhance the initialization of 3DGS models, allowing for faster convergence and better preservation of scene details. Additionally, there is a growing interest in applying Gaussian Mixture Models to physics-aware compression of plasma distribution functions, enabling efficient and accurate simulations. Furthermore, researchers are exploring the use of cycle consistency regularization to improve controllable 3D generation, and developing new frameworks for stylization with disentangled priors on 3D Gaussians. Noteworthy papers include EDGS, which proposes a one-step approximation of scene geometry using triangulated pixels, and Volume Encoding Gaussians, which introduces a novel 3D Gaussian-based representation for scientific volume visualization. Other notable works include EG-Gaussian, which enhances 3D scene reconstruction using epipolar geometry and graph networks, and Metamon-GS, which improves rendering performance through variance-guided densification and light encoding.