Amodal Panoptic Segmentation

Amodal panoptic segmentation aims to create a complete, scene understanding by identifying both visible and occluded objects, combining semantic segmentation of background ("stuff") and instance segmentation of foreground objects ("things"). Current research focuses on developing models that accurately predict both visible and occluded regions, often employing transformer-based architectures and techniques like optimal transport for efficient learning from limited annotations. This research is significant for advancing robotics and autonomous systems by enabling more robust scene understanding and improved navigation capabilities, particularly in complex, cluttered environments.