The field of point cloud processing is rapidly evolving, with a focus on developing innovative methods for denoising, segmentation, and reconstruction. Recent research has explored the use of deep learning approaches, such as neural networks and transformers, to improve the accuracy and efficiency of point cloud processing. These methods have shown promising results in various applications, including computer graphics, autonomous driving, and medical imaging. Notable papers in this area include: A Survey of Deep Learning-based Point Cloud Denoising, which provides a comprehensive review of deep learning-based point cloud denoising methods. Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenes, which proposes a novel approach to open-set segmentation using a reconstruction-based method. Rethinking the Detail-Preserved Completion of Complex Tubular Structures based on Point Cloud, which establishes a new benchmark for tubular structure completion and proposes a novel network for reconnecting discontinuous structures. Towards Training-Free Underwater 3D Object Detection from Sonar Point Clouds, which develops and compares two paradigms for training-free detection of artificial structures in multibeam echo-sounder point clouds. RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation, which solves three unsolved problems in current deep learning-based approaches for roof plane segmentation. GReAT: leveraging geometric artery data to improve wall shear stress assessment, which investigates whether a large dataset of geometric models of 3D blood vessels can benefit wall shear stress assessment in coronary artery models. Multimodal Conditional MeshGAN for Personalized Aneurysm Growth Prediction, which proposes a multimodal conditional mesh-to-mesh generative adversarial network for 3D aneurysm growth prediction. Multispectral LiDAR data for extracting tree points in urban and suburban areas, which explores tree point extraction using multispectral LiDAR and deep learning models. IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detection, which proposes an ensemble framework that synergizes 2D pretrained expert with 3D expert models for surface anomaly detection.