Vanishing Points Estimation and Line Classification in a Manhattan World

The problem of estimating vanishing points for visual scenes under the Manhattan world assumption [1, 2] has been addressed for more than a decade. Surprisingly, the special characteristic of the Manhattan world that lines should be orthogonal or parallel to each other is seldom well utilized. In this paper, we present an algorithm that accurately and efficiently estimates vanishing points and classifies lines by thoroughly taking advantage of this simple fact in the Manhattan world with a calibrated camera. We first present a one-unknown-parameter representation of the 3D line direction in the camera frame. Then derive a quadratic which is employed to solve three orthogonal vanishing points formed by a line triplet. Finally, we develop a RANSAC-based approach to fulfill the task. The performance of proposed approach is demonstrated on the York Urban Database[3] and compared to the state-of-the-art method.

[1]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[2]  Pushmeet Kohli,et al.  Geometric Image Parsing in Man-Made Environments , 2010, ECCV.

[3]  D. Aguileraa,et al.  A NEW METHOD FOR VANISHING POINTS DETECTION IN 3 D RECONSTRUCTION FROM A SINGLE VIEW , 2005 .

[4]  James H. Elder,et al.  Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery , 2008, ECCV.

[5]  Wei Zhang,et al.  Video Compass , 2002, ECCV.

[6]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[7]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[8]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[9]  F. Dellaert,et al.  Atlanta world: an expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments , 2004, CVPR 2004.

[10]  Homer H. Chen.  Pose Determination from Line-to-Plane Correspondences: Existence Condition and Closed-Form Solutions , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Stergios I. Roumeliotis,et al.  Globally optimal pose estimation from line correspondences , 2011, 2011 IEEE International Conference on Robotics and Automation.

[12]  Jean-Philippe Tardif,et al.  Non-iterative approach for fast and accurate vanishing point detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[13]  W. Förstner.  OPTIMAL VANISHING POINT DETECTION AND ROTATION ESTIMATION OF SINGLE IMAGES FROM A LEGOLAND SCENE , 2010 .

[14]  Alan L. Yuille,et al.  Manhattan World: compass direction from a single image by Bayesian inference , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  Rafael Grompone von Gioi,et al.  LSD: A Fast Line Segment Detector with a False Detection Control , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Carsten Rother,et al.  A New Approach for Vanishing Point Detection in Architectural Environments , 2000, BMVC.

[17]  James M. Coughlan,et al.  Manhattan World: Orientation and Outlier Detection by Bayesian Inference , 2003, Neural Computation.

[18]  Markus Vincze,et al.  Vanishing Point Detection in Complex Man-made Worlds , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[19]  Pascal Vasseur,et al.  Globally optimal line clustering and vanishing point estimation in Manhattan world , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Roberto Cipolla,et al.  Camera Calibration from Vanishing Points in Image of Architectural Scenes , 1999, BMVC.

[21]  Stergios I. Roumeliotis,et al.  Optimal estimation of vanishing points in a Manhattan world , 2011, 2011 International Conference on Computer Vision.