Recent developments in large-scale tie-point matching

[1]  Samir Khuller,et al.  Approximation Algorithms for Connected Dominating Sets , 1996, Algorithmica.

[2]  Andrew Zisserman,et al.  Wide baseline stereo matching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[3]  Andrew Zisserman,et al.  Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?" , 2002, ECCV.

[4]  Helmut Mayer.  Robust Orientation, Calibration, and Disparity Estimation of Image Triplets , 2003, DAGM-Symposium.

[5]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[6]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

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

[8]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[9]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  Changchang Wu,et al.  SiftGPU : A GPU Implementation of Scale Invariant Feature Transform (SIFT) , 2007 .

[11]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[12]  Michael Isard,et al.  Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[13]  H. Hirschmüller.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Richard I. Hartley,et al.  Optimised KD-trees for fast image descriptor matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

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

[17]  Richard Szeliski,et al.  Skeletal graphs for efficient structure from motion , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[19]  Michal Havlena,et al.  Randomized structure from motion based on atomic 3D models from camera triplets , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Jean-Philippe Pons,et al.  Towards high-resolution large-scale multi-view stereo , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  O. Chum,et al.  Geometric min-Hashing: Finding a (thick) needle in a haystack , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Richard Szeliski,et al.  Building Rome in a day , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[23]  Michal Havlena,et al.  Efficient Structure from Motion by Graph Optimization , 2010, ECCV.

[24]  Marc Pollefeys,et al.  Fast robust large-scale mapping from video and internet photo collections , 2010 .

[25]  Jan-Michael Frahm,et al.  Building Rome on a Cloudless Day , 2010, ECCV.

[26]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[27]  Daniel P. Huttenlocher,et al.  Location Recognition Using Prioritized Feature Matching , 2010, ECCV.

[28]  Yongjun Zhang,et al.  Relative orientation based on multi-features , 2011 .

[29]  C. Heipke,et al.  Multi-view dense matching supported by triangular meshes , 2011 .

[30]  Tomás Pajdla,et al.  Multi-view reconstruction preserving weakly-supported surfaces , 2011, CVPR 2011.

[31]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[32]  Jan-Michael Frahm,et al.  Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs , 2008, International Journal of Computer Vision.

[33]  Jiri Matas,et al.  Learning Vocabularies over a Fine Quantization , 2013, International Journal of Computer Vision.

[34]  Uwe Soergel,et al.  Matching of straight line segments from aerial stereo images of urban areas , 2012 .

[35]  Bo Wu,et al.  Integrated point and edge matching on poor textural images constrained by self-adaptive triangulations , 2012 .

[36]  Andrew Zisserman,et al.  Three things everyone should know to improve object retrieval , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Konrad Schindler,et al.  Optimal Reduction of Large Image Databases for Location Recognition , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[38]  Changchang Wu,et al.  Towards Linear-Time Incremental Structure from Motion , 2013, 2013 International Conference on 3D Vision.

[39]  Miriam Cabrelles,et al.  Automatic orientation and 3D modelling from markerless rock art imagery , 2013 .

[40]  K. Wenzel,et al.  A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs , 2013 .

[41]  Jan Boehm,et al.  Close-Range Photogrammetry and 3D Imaging , 2013 .

[42]  Konrad Schindler,et al.  Predicting Matchability , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[43]  Konrad Schindler,et al.  VocMatch: Efficient Multiview Correspondence for Structure from Motion , 2014, ECCV.

[44]  C. Stentoumis,et al.  On accurate dense stereo-matching using a local adaptive multi-cost approach , 2014 .

[45]  Jan-Michael Frahm,et al.  Reconstructing the World* in Six Days *(As Captured by the Yahoo 100 Million Image Dataset) , 2015, CVPR 2015.

[46]  Pascal Fua,et al.  Worldwide Pose Estimation Using 3D Point Clouds , 2012, ECCV.