Occlusion Free

Occlusion-free processing aims to reconstruct scenes and objects as they would appear without any obstructions, addressing a major limitation in various computer vision and robotics tasks. Current research focuses on developing algorithms and models, such as neural radiance fields and point cloud-based methods, that leverage multiple viewpoints or temporal information to infer occluded regions, often incorporating strategies like visibility field estimation and advanced upsampling techniques. This work is significant for improving the accuracy and robustness of applications ranging from 3D scene reconstruction and autonomous navigation to gait recognition and visual servoing in robotics.