Amodal Segmentation

Amodal segmentation aims to predict the complete shape of objects, including their occluded parts, from a single image or video frame. Current research focuses on leveraging deep learning models, including diffusion models and convolutional networks, often incorporating shape priors or attention mechanisms to improve accuracy, particularly in challenging scenarios with significant occlusion. This field is crucial for advancing various applications, such as robotic grasping, autonomous driving, and medical image analysis, where understanding complete object shapes despite occlusions is essential for robust performance. The development of new datasets with amodal annotations, including those generated synthetically, is also a significant area of ongoing work.