Occlusion Inference

Occlusion inference focuses on computationally reconstructing or predicting information hidden from view, a crucial challenge across diverse fields like autonomous driving and robotics. Current research emphasizes developing models that leverage multiple data sources (e.g., temporal information, social cues, and vectorized scene representations) and employ advanced architectures such as transformers and neural rendering techniques to improve accuracy and robustness in handling occlusions. These advancements are significantly impacting applications requiring reliable perception in partially observable environments, enabling safer autonomous navigation, more accurate object detection in aerial imagery, and improved 3D human pose estimation from video.