Dense matching of multiple wide-baseline views

This paper describes a PDE-based method for dense depth extraction from multiple wide-baseline images. Emphasis lies on the usage of only a small amount of images. The integration of these multiple wide-baseline views is guided by the relative confidence that the system has in the matching to different views. This weighting is fine-grained in that it is determined for every pixel at every iteration. Reliable information spreads fast at the expense of less reliable data, both in terms of spatial communications within a view and in terms of information exchange between the views. Changes in intensity between images can be handled in a similar fine grained fashion.

[1]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[2]  Luc Van Gool,et al.  Determination of Optical Flow and its Discontinuities using Non-Linear Diffusion , 1994, ECCV.

[3]  Michael F. Cohen,et al.  Minimal Surfaces for Stereo , 2002, ECCV.

[4]  Luc Van Gool,et al.  Motion - Stereo Integration for Depth Estimation , 2002, ECCV.

[5]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Joachim Weickert,et al.  Theoretical Foundations of Anisotropic Diffusion in Image Processing , 1994, Theoretical Foundations of Computer Vision.

[7]  Rachid Deriche,et al.  Dense Disparity Map Estimation Respecting Image Discontinuities: A PDE and Scale-Space BasedApproach , 2002, MVA.

[8]  Kiriakos N. Kutulakos,et al.  A Theory of Shape by Space Carving , 2000, International Journal of Computer Vision.

[9]  Maarten Vergauwen,et al.  A Hierarchical Symmetric Stereo Algorithm Using Dynamic Programming , 2002, International Journal of Computer Vision.

[10]  Joachim Weickert,et al.  Anisotropic diffusion in image processing , 1996 .

[11]  Ronald W. Schafer,et al.  Image-based photo hulls , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[12]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Luc Van Gool,et al.  Wide-baseline multiple-view correspondences , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[14]  Luc Van Gool,et al.  Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions , 2000, BMVC.

[15]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[16]  Otmar Scherzer,et al.  Inverse Problems, Image Analysis, and Medical Imaging , 2002 .

[17]  Adam Baumberg,et al.  Reliable feature matching across widely separated views , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[18]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[19]  Vladimir Kolmogorov,et al.  Multi-camera Scene Reconstruction via Graph Cuts , 2002, ECCV.

[20]  Luc Van Gool,et al.  PDE-based multi-view depth estimation , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[21]  M. Vergauwen,et al.  A hierarchical stereo algorithm using dynamic programming , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[22]  Olivier D. Faugeras,et al.  Complete Dense Stereovision Using Level Set Methods , 1998, ECCV.

[23]  T. Brox,et al.  Diffusion and regularization of vector- and matrix-valued images , 2002 .

[24]  Rachid Deriche,et al.  Optical-Flow Estimation while Preserving Its Discontinuities: A Variational Approach , 1995, ACCV.

[25]  Joachim Weickert,et al.  Reliable Estimation of Dense Optical Flow Fields with Large Displacements , 2000, International Journal of Computer Vision.