The field of point cloud processing is moving towards more efficient compression algorithms and innovative analysis techniques. Recent developments focus on improving the reconstruction quality and rate-distortion performance of compressed point clouds, particularly in applications such as virtual reality and immersive communication. Researchers are exploring new methods, including the integration of Wiener filters and generative diffusion priors, to enhance the compression of geometry and color attributes. Moreover, voxel-based approaches are being improved with the introduction of space-to-channel context models and Geometry Residual Coding. Noteworthy papers in this area include UniPCGC, which proposes a unified framework for point cloud geometry compression supporting lossy and lossless compression, variable rate, and complexity. Another notable work is the Unified Geometry and Color Compression Framework, which uses generative diffusion priors to compress colored point clouds. These advancements have the potential to significantly improve the efficiency and effectiveness of point cloud processing, enabling wider adoption in various applications.