The recent advancements in the field of 3D point cloud processing have seen a significant shift towards more efficient and integrated frameworks. Researchers are increasingly focusing on combining top-down and bottom-up approaches to enhance both the accuracy and efficiency of tasks such as 3D visual grounding and point cloud registration. These hybrid methods aim to leverage the strengths of each approach while mitigating their respective weaknesses, leading to state-of-the-art performance in benchmarks. Additionally, there is a growing emphasis on developing plug-and-play models for point cloud upsampling that not only improve the density and uniformity of point clouds but also reduce processing time and resource consumption. The integration of attention mechanisms and probabilistic models in point cloud attribute compression is another notable trend, offering improved compression efficiency and fidelity. Overall, the field is progressing towards more streamlined and effective solutions that balance performance with computational efficiency, paving the way for broader practical applications in robotics, computer vision, and beyond.