The field of 3D Gaussian Splatting (3DGS) is witnessing significant developments, with a focus on improving rendering efficiency, novel view synthesis, and perceptual quality assessment. Researchers are exploring innovative methods to overcome the limitations of 3DGS, such as memory constraints and view selection strategies. Noteworthy papers in this area include CLM, which enables 3DGS to render large scenes on a single consumer-grade GPU, and UltraGS, which proposes a Gaussian Splatting framework optimized for ultrasound imaging. Other notable works include SkelSplat, which introduces a novel framework for multi-view 3D human pose estimation, and OUGS, which presents a principled uncertainty formulation for 3DGS. These advancements are pushing the boundaries of 3DGS and its applications in various fields.