Current methods for 2D and 3D object understanding struggle with severe
occlusions in busy urban environments, partly due to the lack of large-scale
labeled ground-truth annotations for learning occlusion. In this work, we
introduce a novel framework for automatically generating a large, realistic
dataset of dynamic objects under occlusions using freely available time-lapse
imagery. By leveraging off-the-shelf 2D (bounding box, segmentation, keypoint)
and 3D (pose, shape) predictions as pseudo-groundtruth, unoccluded 3D objects
are identified automatically and composited into the background in a clip-art
style, ensuring realistic appearances and physically accurate occlusion
configurations. The resulting clip-art image with pseudo-groundtruth enables
efficient training of object reconstruction methods that are robust to
occlusions. Our method demonstrates significant improvements in both 2D and 3D
reconstruction, particularly in scenarios with heavily occluded objects like
vehicles and people in urban scenes.

该研究提出了一种新的框架，通过利用自由获得的延时摄影图像来自动生成大规模真实的动态物体遮挡数据集，并通过伪地面实现有效的物体重建方法的训练，取得了在 2D 和 3D 重建方面显著改进的成果。