The field of point cloud processing and analysis is rapidly evolving, with a focus on developing innovative methods for improving the robustness and accuracy of 3D deep learning models. Recent research has explored the use of novel frameworks and techniques, such as medial axis transform and diffusion models, to enhance the transferability and undefendability of point cloud attacks. Additionally, there is a growing interest in developing unified multimodal frameworks for bridging 2D and 3D industrial anomaly detection, as well as refining point cloud registration algorithms via zero-shot learning. Noteworthy papers in this area include: PDT, which presents a novel framework for point distribution transformation with diffusion models, successfully transforming input point clouds into various forms of structured outputs. RARE, which proposes a novel zero-shot method for refining point cloud registration algorithms, leveraging correspondences derived from depth images to enhance point feature representations. MAT-Adv, which introduces a novel adversarial attack framework that enhances both transferability and undefendability by explicitly perturbing the medial axis transform representations.