3D Object Trajectory Reconstruction using Instance-Aware Multibody Structure from Motion and Stereo Sequence Constraints

Three-dimensional environment perception is a key element of autonomous driving and driver assistance systems. A common image based approach to determine three-dimensional scene information is stereo matching, which is limited by the stereo camera baseline. In contrast to stereo matching based methods, we present an approach to reconstruct three-dimensional object trajectories combining temporal adjacent views for object point triangulation. We track two-dimensional object shapes on pixel level exploiting instance-aware semantic segmentation techniques and optical flow cues. We apply Structure from Motion (SfM) to object and background images to determine initial camera poses relative to object instances as well as background structures and refine the initial SfM results by integrating stereo camera constraints using factor graphs. We compute object trajectories using stereo sequence constraints of object and background reconstructions. We show qualitative results using publicly available video data of driving sequences. Due to the lack of suitable ground truth, we create a synthetic benchmark dataset of stereo sequences with vehicles in urban environments. Our algorithm achieves an average trajectory error of 0.09 meter using the dataset. The dataset is on our website
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publicly available.