Despite recent progress, reconstructing outdoor scenes in 3D from movable platforms remains a highly difficult endeavor. Challenges include low frame rates, occlusions, large distortions and difficult lighting conditions. In this project, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows us to reduce noise and complete missing surfaces as objects of similar shape benefit from all observations for the respective category.


The video below illustrates our approach and demonstrates its ability to complete missing surfaces. Best viewed using YouTube's 720p HD setting.

The source code for this project has been tested on Ubuntu 14.04 and Matlab 2013b and is published under the GNU General Public License. If you find this project useful, we would be happy if you cite us:
@inproceedings{Zhou2015ICCV,
  author = {Chen Zhou and Fatma Güney and Yizhou Wang and Andreas Geiger},
  title = {Exploiting Object Similarity in 3D Reconstruction},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year = {2015}
}