Line Matching Jonghee Park GIST CV-Lab. Lines Fundamental feature in many computer vision fields 3D reconstruction, SLAM, motion estimation Useful features in low-textured and man-made structure Stereo Technology Assume that depth discontinuity mainly occurs nearby edges Applied vehicle, robot and aerial systems because of its low cost and depth information To apply lines into practical stereo systems, computational complexity is critical issue for real-time performance. Introduction 2 Line matching with pre-processing Usually, point matching is conducted to find geometric relations between features and scenes Scale and rotation [CVPR12], fundamental matrix and tri-pocal tensor [CVPR97] Point matching takes long time because of feature extraction in scale space and construction of HOG Line matching without pre-processing Grouping based matching Make groups or clusters lines for distinctive similarity measure according to the topological relation LS[ICCV09] takes long time because of intensive 2D search for making multiple clusters Individual matching Match lines individually without topological relation MSLD[PR12] makes multiple HOG for a line Previous works 3 MSLD: A robust descriptor for line matching Zhiheng Wang, Fuchao Wu, and Zhanyi Hu National Lab. Of Pattern Recognition PR 2009 Jonghee Park GIST CV-Lab. Pixel Support Region 5 Orientation Rotation relation of gradients between two images Approximation Weighting with gaussian kernel like SIFT Interpolation along orientation direction for boundary effect Sub-region Representation 6 Gradient Distance from line Each sub-region has following 4 dimension feature vector Gradient description matrix To cover line length variantion Sub-region Representation 7 MSLD Matching result 8 Line Matching with Binary Costs Jonghee Park GIST CV-Lab. Comparison 10 Matching Results 11