Contact: Rui Wang, Nan Yang, Jörg Stückler, Prof. Daniel Cremers

This page is still under construction. Stay tuned.

Scene understanding from images is a challenging problem which is encountered in autonomous driving. On the object level, while 2D methods have gradually evolved from computing simple bounding boxes to delivering finer grained results like instance segmentations, the 3D family is still dominated by estimating 3D bounding boxes. In this paper, we propose a novel approach to jointly infer the 3D rigid-body poses and shapes of vehicles from a stereo image pair using shape priors. Unlike previous works that geometrically align shapes to point clouds from dense stereo reconstruction, our approach works directly on images by combining a photometric and a silhouette alignment term in the energy function. An adaptive sparse point selection scheme is proposed to efficiently measure the consistency with both terms. In experiments, we show superior performance of our method on 3D pose and shape estimation over the previous geometric approach. Moreover, we demonstrate that our method can also be applied as a refinement step and significantly boost the performances of several state-of-the-art deep learning based 3D object detectors.

The video is with audio.

If you find our work useful in your research, please consider citing:

@InProceedings{wang2020directshape,
  author={R. Wang and N. Yang and J. Stueckler and D. Cremers},
  title={DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation},
  booktitle={Proc. of the IEEE International Conference on Robotics and Automation (ICRA)},
  year={2020}
}

• ICRA 2020 paper: paper. Derivation of all the analytical Jacobians and more qualitative results are provided in: supplementary document. They are also available on arxiv.
  
• Validation splits of KITTI Object 3D: val1.txt (used by Mono3D, 3DOP and MLF), val2.txt (used by Deep3DBox).
  
• 3D pose evaluation results on KITTI Object 3D: tba
  
• 3D shape evaluation results on KITTI Stereo 2015: tba
  
• Please contact Rui Wang if you need anything further.

• Shape variation by modifying shape coefficients with color coded signed distances to the surface:

• Sample qualitative results (more can be found in the supplementary document above):

Export as PDF, XML, TEX or BIB

Conference and Workshop Papers
2020
[]		DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (R. Wang, N. Yang, J. Stueckler and D. Cremers), In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2020. ([video][presentation][project page][supplementary][arxiv]) [bibtex] [pdf]