Panoptic PartFormer

Panoptic segmentation aims to comprehensively understand scenes by simultaneously identifying both individual objects ("things") and their surrounding regions ("stuff"), a crucial step for applications like autonomous driving and robotics. Recent research focuses on developing efficient and accurate models, often employing transformer-based architectures like PartFormer and its variants, to achieve real-time performance across diverse data modalities (images, LiDAR point clouds). These advancements are driving progress in high-resolution 3D mapping and improving the robustness and speed of scene understanding in complex environments.