Amodal Completion

Amodal completion in computer vision aims to reconstruct the invisible portions of objects occluded in an image, mirroring the human brain's ability to perceive complete objects despite partial visibility. Current research focuses on developing models, often employing self-supervised learning and architectures like transformers and diffusion models, to predict both the shape and appearance of occluded regions using visible parts and contextual information. This research is significant for advancing object recognition, scene understanding, and applications such as robotic manipulation and augmented reality, where accurate perception of partially visible objects is crucial.