Line Matching Across Views Based on Multiple View Stereo
-
摘要: 提出了一种基于多视立体视觉(Multiple view stereo,MVS)进行多视图直线匹配的方法. 本文方法首先利用MVS所得到的三维点云及其可见性信息,建立三维点与图像直线的对应关系. 根据此对应关系,为每条图像直线建立由一个三维点集和一个三维单位向量构成的描述子,用以衡量图像直线之间的相似性及一致性. 之后,本文方法以所有图像直线为顶点建立一个图,并引入了图谱分析来获取统一的顶点距离度量. 最后,本方法对DBSCAN聚类算法进行了修改,并用修改后的算法从图谱分析结果中获取可靠的直线匹配. 实验显示,本方法比已有方法更加鲁棒,并且有更高的准确率.Abstract: A graph-based multiple view line matching method is proposed based on results of multiple view stereo (MVS) algorithms. With the 3D points and their visibility information provided by MVS, point-line correspondences are firstly established through 3D-to-2D re-projection. Each image line detected in different views is described using a 3D point set as well as a unit vector representing its coarse 3D direction. From such a description, pairwise similarity and consistency are evaluated. Then, a graph is constructed to contain all image lines as nodes. To get a unified node distance measure, a spectral graph analysis method is employed. Finally, a modified DBSCAN algorithm is introduced to obtain reliable line matches from the graph. Experiments show that our method is more robust and exhibits better accuracy than the existing methods.
-
[1] Snavely N, Seitz S M, Szeliski R. Photo tourism: exploring photo collections in 3D. ACM Transactions on Graphics, 2006, 25(3): 835-846 [2] [2] Furukawa Y, Ponce J. Accurate, dense, and robust multiview stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(8): 1362-1376 [3] [3] Snavely N, Seitz S M, Szeliski R. Modeling the world from internet photo collections. International Journal of Computer Vision, 2008, 80(2): 189-210 [4] [4] Werner T, Zisserman A. New techniques for automated architectural reconstruction from photographs. In: Proceedings of the 7th European Conference on Computer Vision. London, UK: Springer-Verlag, 2002. 541-555 [5] [5] Aider O A, Hoppenot P, Colle E. A model-based method for indoor mobile robot localization using monocular vision and straight-line correspondences. Robotics and Autonomous Systems, 2005, 52(2-3): 229-246 [6] [6] Fan B, Wu F C, Hu Z Y. Robust line matching through line-point invariants. Pattern Recognition, 2012, 45(2): 794-805 [7] [7] Bay H, Ferraris V, Van Gool L. Wide-baseline stereo matching with line segments. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington DC, USA: IEEE, 2005. 329-336 [8] [8] Wang Z H, Wu F C, Hu Z Y. Msld: a robust descriptor for line matching. Pattern Recognition, 2009, 42(5): 941-953 [9] [9] Lpez J, Fucios M, Fdez-Vidal X R, Pardo X M. Detection and matching of lines for close-range photogrammetry. In: Proceedings of the 6th Iberian Conference on Pattern Recognition and Image Analysis. Madeira, Portugal: Springer, 2013. 732-739 [10] Wang L, Neumann U, You S. Wide-baseline image matching using line signatures. In: Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009. 1311-1318 [11] Zhang L L, Koch R. An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency. Journal of Visual Communication and Image Representation, 2013, 24(7): 794-805 [12] Schmid C, Zisserman A. Automatic line matching across views. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico: IEEE, 1997. 666-671 [13] Schmid C, Zisserman A. The geometry and matching of lines and curves over multiple views. International Journal of Computer Vision, 2000, 40(3): 199-233 [14] Heuel S, Frstner W. Matching, reconstructing and grouping 3d lines from multiple views using uncertain projective geometry. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, USA: IEEE, 2001. II517-II524 [15] Elaksher A F. Automatic line matching across multiple views based on geometric and radiometric properties. Applied Geomatics, 2011, 3(1): 23-33 [16] Hartley R I, Zisserman A. Multiple View Geometry in Computer Vision (2nd edition). Cambridge: Cambridge University Press, 2004. 321-323 [17] Xiao J X, Fang T, Tan P, Zhao P, Ofek E, Quan L. Image-based facade modeling. ACM Transactions on Graphics, 2008, 27(5): Article No. 161 [18] Chen T W, Wang Q. 3d line segment detection for unorganized point clouds from multi-view stereo. In: Proceedings of the 10th Asian conference on Computer vision. Queenstown, New Zealand: Springer-Verlag, 2010. 400-411 [19] Sander J, Ester M, Kriegel H P, Xu X W. Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 1998, 2(2): 169-194 [20] Saerens M, Fouss F, Yen L, Dupont P. The principal components analysis of a graph, and its relationships to spectral clustering. In: Proceedings of the 15th European Conference on Machine Learning. Pisa, Italy: Springer-Verlag, 2004. 371-383 [21] Daszykowski M, Walczak B, Massart D L. Looking for natural patterns in data: Part 1. Density-based approach. Chemometrics and Intelligent Laboratory Systems, 2001, 56(2): 83-92 [22] Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698
点击查看大图
计量
- 文章访问数: 2769
- HTML全文浏览量: 98
- PDF下载量: 2047
- 被引次数: 0