Foreground Occlusion

Foreground occlusion, the obstruction of background objects by foreground elements, is a significant challenge in computer vision, with research focused on reconstructing the hidden background. Current approaches leverage diverse data sources, including multi-view images from traditional and neuromorphic (event-based) cameras, and light fields, employing techniques like deep learning models (e.g., convolutional neural networks, spiking neural networks) to integrate information from multiple perspectives and remove occlusions. These advancements are improving the quality of occlusion removal, particularly in scenarios with dense occlusions and challenging lighting conditions, with implications for applications ranging from autonomous driving to augmented reality.