Region-Based Epipolar and Planar Geometry Estimation in Low─Textured Environments

Given two views of the same scene, usual correspondence geometry estimation techniques exploit the well-established effectiveness of keypoint descriptors. However, such features have a hard time in poorly textured man-made environments, possibly containing repetitive patterns and/or specularities, such as industrial places. In that paper, we propose a novel method for two-view epipolar and planar geometry estimation that first aims at detecting and matching physical vertical planes frequently present in these environments, before estimating corresponding homographies. Inferred local correspondences are finally used to improve fundamental matrix estimation. The gain in precision is demonstrated on industrial and urban environments.

[1]  Peter I. Corke,et al.  Visual Place Recognition: A Survey , 2016, IEEE Transactions on Robotics.

[2]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.

[3]  Reinhard Koch,et al.  An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency , 2013, J. Vis. Commun. Image Represent..

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Zhanyi Hu,et al.  Robust line matching through line-point invariants , 2012, Pattern Recognit..

[6]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  In-So Kweon,et al.  Probabilistic matching of lines for their homography , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

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

[9]  Jitendra Malik,et al.  Generic 3D Representation via Pose Estimation and Matching , 2016, ECCV.

[10]  Marie-Odile Berger,et al.  A Simple and Effective Method to Detect Orthogonal Vanishing Points in Uncalibrated Images of Man-Made Environments , 2016, Eurographics.

[11]  Michael Milford,et al.  Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free , 2015, Robotics: Science and Systems.

[12]  Xosé R. Fernández-Vidal,et al.  Two-view line matching algorithm based on context and appearance in low-textured images , 2015, Pattern Recognit..

[13]  Pascal Monasse,et al.  Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions , 2016, ECCV.

[14]  Lionel Moisan,et al.  Fundamental Matrix of a Stereo Pair, with A Contrario Elimination of Outliers , 2016, Image Process. Line.

[15]  Lu Wang,et al.  Wide-baseline image matching using Line Signatures , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[16]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[17]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[18]  Vincent Lepetit,et al.  LIFT: Learned Invariant Feature Transform , 2016, ECCV.

[19]  Yan Lu,et al.  A two-view based multilayer feature graph for robot navigation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[20]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[21]  Haojie Li,et al.  Novel Coplanar Line-Points Invariants for Robust Line Matching Across Views , 2016, ECCV.

[22]  Zhanyi Hu,et al.  Line matching leveraged by point correspondences , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Luc Van Gool,et al.  Wide-baseline stereo matching with line segments , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  Rafael Grompone von Gioi,et al.  LSD: a Line Segment Detector , 2012, Image Process. Line.