3D Parsing

3D parsing aims to decompose a scene into its constituent parts, representing both their semantic labels and geometric properties in three dimensions. Current research focuses on developing robust methods for 3D shape reconstruction from various data sources, including single images and point clouds, often employing neural networks such as convolutional neural networks (CNNs) and transformers, along with techniques like signed distance functions (SDFs) and mesh processing. These advancements are driving progress in applications ranging from autonomous driving and robotics to medical image analysis and 3D modeling, enabling more sophisticated scene understanding and interaction. The field is also exploring efficient and unsupervised learning approaches to handle large-scale, unannotated datasets.