Research Editor selected
Occlusion for Free: Detecting Occlusion from the Disagreement Between Two Monocular Distance Cues
The paper presents a method to detect occlusion using the disagreement between two monocular distance cues—apparent height and metric depth—that a camera-only pipeline already computes. It requires no training or occlusion labels and achieves high AUC scores on KITTI (0.909) and CARLA (0.819), outperforming distance alone. The approach is robust to domain shifts and does not rely on focal length, stature, or depth scale.
Why it matters: Offers a practical, label-free occlusion detection method that works across datasets and deployment settings, addressing a key gap in real-world robotics and autonomous driving.
occlusion detectionmonocular depth estimationautonomous driving