The field of point cloud compression and image anonymization is rapidly evolving, with a focus on developing innovative methods to balance compression efficiency, privacy preservation, and performance. Recent research has explored the use of implicit neural representations, learnable activation functions, and switchable priors to improve compression efficiency and reduce computational complexity. Additionally, novel approaches to image anonymization, such as object scrubbing and penalty-driven anonymization, have shown promising results in preserving privacy while maintaining performance. Noteworthy papers in this area include:

• Universal Representations for Classification-enhanced Lossy Compression, which introduced a universal encoder for multiple decoding objectives.
• PointKAN, which applied Kolmogorov-Arnold Networks to point cloud analysis tasks and achieved state-of-the-art results on benchmark datasets.
• Beyond Anonymization: Object Scrubbing for Privacy-Preserving 2D and 3D Vision Tasks, which demonstrated the effectiveness of object removal in preserving dataset utility. These advances have significant implications for applications such as autonomous driving, virtual reality, and robotics, and are expected to continue shaping the direction of research in this field.