De Occlusion

De-occlusion research focuses on computationally reconstructing missing or obscured parts of images, addressing challenges like masked faces, occluded lanes in autonomous driving, and hidden hand or body parts in various applications. Current approaches leverage generative adversarial networks (GANs), transformers, and graph convolutional networks, often incorporating semantic segmentation and 3D modeling to improve accuracy and robustness. This field is crucial for advancing applications such as facial recognition, autonomous navigation, human-computer interaction, and extended reality experiences by enhancing the reliability and completeness of visual data.