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International Journal of Computer Vision

, Volume 127, Issue 10, pp 1501–1526 | Cite as

Estimation of 3D Category-Specific Object Structure: Symmetry, Manhattan and/or Multiple Images

  • Yuan GaoEmail author
  • Alan L. Yuille
Article
  • 229 Downloads

Abstract

Many man-made objects have intrinsic symmetries and often Manhattan structure. By assuming an orthographic or a weak perspective projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, for the two cases when the input is a single image or multiple images from the same category, e.g. multiple different cars from various viewpoints. More specifically, analysis on the single image case shows that Manhattan alone is sufficient to recover the camera projection and then the 3D structure can be reconstructed uniquely by exploiting symmetry. But Manhattan structure can be hard to observe from a single image due to occlusion. Hence, we extend to the multiple-image case which can also exploit symmetry but does not require Manhattan structure. We propose novel structure from motion methods for both rigid and non-rigid object deformations, which exploit symmetry and use multiple images from the same object category as input. We perform experiments on the Pascal3D+ dataset with either human labeled 2D keypoints or with 2D keypoints localized from a convolutional neural network. The results show that our methods which exploit symmetry significantly outperform the baseline methods.

Keywords

Symmetry Manhattan Single image Symmetric rigid structure from motion Symmetric non-rigid structure from motion 

Notes

Acknowledgements

We would like to thank Ehsan Jahangiri, Cihang Xie, Weichao Qiu, Xuan Dong, Siyuan Qiao for giving feedbacks on the manuscript. This work was partially supported by ARO 62250-CS, ONR N00014-15-1-2356, and the NSF award CCF-1317376.

Supplementary material

11263_2019_1195_MOESM1_ESM.pdf (269 kb)
Supplementary material 1 (pdf 268 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina
  2. 2.Tencent AI LabShenzhenChina
  3. 3.Departments of Computer Science and Cognitive ScienceJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of StatisticsUCLALos AngelesUSA

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