Partial Occlusion

Partial occlusion, the obstruction of part of an object's view, presents a significant challenge across numerous computer vision and robotics applications. Current research focuses on developing robust methods for handling occlusions in various tasks, including object recognition, pose estimation, and trajectory prediction, often employing deep learning architectures like convolutional neural networks (CNNs), transformers, and graph convolutional networks (GCNs), along with novel data augmentation techniques to address data scarcity. These advancements are crucial for improving the reliability and safety of autonomous systems, enhancing medical image analysis, and advancing human-computer interaction in virtual and augmented reality environments. The development of more accurate and efficient occlusion-handling techniques is driving progress in numerous fields.