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Omnidirectional 3D Reconstruction in Augmented Manhattan Worlds


Conference Paper



This paper proposes a method for high-quality omnidirectional 3D reconstruction of augmented Manhattan worlds from catadioptric stereo video sequences. In contrast to existing works we do not rely on constructing virtual perspective views, but instead propose to optimize depth jointly in a unified omnidirectional space. Furthermore, we show that plane-based prior models can be applied even though planes in 3D do not project to planes in the omnidirectional domain. Towards this goal, we propose an omnidirectional slanted-plane Markov random field model which relies on plane hypotheses extracted using a novel voting scheme for 3D planes in omnidirectional space. To quantitatively evaluate our method we introduce a dataset which we have captured using our autonomous driving platform AnnieWAY which we equipped with two horizontally aligned catadioptric cameras and a Velodyne HDL-64E laser scanner for precise ground truth depth measurements. As evidenced by our experiments, the proposed method clearly benefits from the unified view and significantly outperforms existing stereo matching techniques both quantitatively and qualitatively. Furthermore, our method is able to reduce noise and the obtained depth maps can be represented very compactly by a small number of image segments and plane parameters.

Author(s): Miriam Schoenbein and Andreas Geiger
Book Title: International Conference on Intelligent Robots and Systems
Pages: 716 - 723
Year: 2014
Month: October
Publisher: IEEE

Department(s): Autonomous Vision, Perceiving Systems
Bibtex Type: Conference Paper (conference)
Paper Type: Conference

DOI: http://dx.doi.org/10.1109/IROS.2014.6942637
Event Name: IEEE/RSJ International Conference on Intelligent Robots and System
Event Place: Chicago, IL, USA

Address: Chicago, IL, USA
Attachments: pdf


  title = {Omnidirectional 3D Reconstruction in Augmented Manhattan Worlds},
  author = {Schoenbein, Miriam and Geiger, Andreas},
  booktitle = {International Conference on Intelligent Robots and Systems},
  pages = {716 - 723},
  publisher = {IEEE},
  address = {Chicago, IL, USA},
  month = oct,
  year = {2014},
  month_numeric = {10}