The field of 3D scene understanding and reconstruction is rapidly advancing, with a focus on developing more efficient and effective methods for scene representation, object segmentation, and reconstruction. Recent research has explored the use of Gaussian Splatting (3DGS) and other techniques to improve the accuracy and realism of 3D scene reconstruction. Notable developments include the use of reinforcement learning for bottom-up part-wise reconstruction, semantic-driven adaptive Gaussian splatting for extended reality, and geometry-aware assisted depth completion for transparent and specular objects. These innovations have significant implications for applications such as robotics, autonomous systems, and augmented reality. Noteworthy papers in this area include Utilizing Reinforcement Learning for Bottom-Up part-wise Reconstruction of 2D Wire-Frame Projections, which demonstrates the potential of iterative RL wire-frame reconstruction in two dimensions, and SAGE: Semantic-Driven Adaptive Gaussian Splatting in Extended Reality, which effectively reduces memory and computational overhead while keeping a desired target visual quality. Additionally, GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects proposes a geometry-aware assisted depth completion method that outperforms other state-of-the-art methods.