This chapter delves into advanced methods and technologies for point cloud enhancement, primarily focusing on processing challenges such as downsampling, completion, and denoising. It outlines various approaches, including heuristic sampling, learning-based sampling, and key point sampling, to optimize point cloud processing for applications like autonomous driving and virtual reality. Each section not only explains the technical processes involved but also discusses the implications for real-world applications, emphasizing the integration of these technologies into larger intelligent systems. This chapter aims to address the limitations of current technologies and suggests future directions for more robust, efficient, and accurate point cloud processing methods.