  We present a method to reconstruct the three-dimensional trajectory of a
moving instance of a known object category using stereo video data. We track
the two-dimensional shape of objects on pixel level exploiting instance-aware
semantic segmentation techniques and optical flow cues. We apply Structure from
Motion (SfM) techniques to object and background images to determine for each
frame initial camera poses relative to object instances and background
structures. We refine the initial SfM results by integrating stereo camera
constraints exploiting factor graphs. We compute the object trajectory by
combining object and background camera pose information. In contrast to stereo
matching methods, our approach leverages temporal adjacent views for object
point triangulation. As opposed to monocular trajectory reconstruction
approaches, our method shows no degenerated cases. We evaluate our approach
using publicly available video data of vehicles in urban scenes.
