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Home Research Areas Visual SLAM DirectShape: Direct Photometric Alignment of Shape Priors

DirectShape: Direct Photometric Alignment of Shape Priors

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

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Abstract

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.

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  • Derivation of all the analytical Jacobians: tba
  • 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

Results

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Preprints
2019
[]DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation (R. Wang, N. Yang, J. Stueckler and D. Cremers), In preprint, 2019. ([arxiv][video] [project page]) [bibtex]
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