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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich



DirectShape: Direct Photometric Alignment of Shape Priors

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.


ICRA Presentation

The video is with audio.


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

  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)},


  • 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):


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Conference and Workshop Papers
[]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]
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CVPR 2023

We have six papers accepted to CVPR 2023.


NeurIPS 2022

We have two papers accepted to NeurIPS 2022.


WACV 2023

We have two papers accepted at WACV 2023.


Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.


MCML Kick-Off

On July 27th, we are organizing the Kick-Off of the Munich Center for Machine Learning in the Bavarian Academy of Sciences.