3D Panoptic Segmentation

3D panoptic segmentation aims to comprehensively label every point in a 3D scene with both semantic (e.g., "car," "tree") and instance (individual object) information. Current research focuses on developing robust methods that handle diverse data sources (RGB-D images, LiDAR point clouds), address challenges like zero-shot segmentation of unseen objects, and improve efficiency for large-scale scenes. This is achieved through various approaches, including graph-based clustering of point clouds, novel neural network architectures (e.g., double-encoders, NeRF-based methods), and the integration of 2D and 3D information, often leveraging pre-trained models like CLIP. Advances in this field are crucial for applications such as autonomous driving, robotics, and 3D scene understanding.