Make Unseen Clear: Occluder Removal for Complete 3D Pedestrian Detection

Yan Luo, Zefeng Qian, Chongyang Zhang, Yi Wang, Xiaolei Hu

Abstract: In autonomous driving, the ability to detect pedestrians accurately is crucial for safety. Some detectors, however, often struggle with occlusions, where pedestrians partially hidden behind objects appear incomplete and are harder to be identified accurately. To alleviate this issue, we introduce Clear3D, which make previous partially unseen pedestrians visible by effectively removing occluders to enhance 3D pedestrian detection. The first essential step in Clear3D is to accurately identify occluded regions. This is achieved by calculating the intensity variation along the LiDAR ray direction, where regions with small changes are classified as occluded. After identifying the occluded areas, they are transformed as masks and fed into the inpainting module, reconstructing the originally unseen parts. Above process, encompassing both the perception and reconstruction of occluded regions, is termed occluder removal. Finally, by fusing the recovered occlusion-free image features with LiDAR features, Clear3D generates more complete representation, enhancing the performance of downstream 3D classification and localization tasks. Extensive evaluations show that Clear3D achieves state-of-the-art accuracy across various challenging settings on KITTI, particularly in heavy occlusion environments.

External IDs:dblp:conf/icassp/LuoQZ0H25