An Improved Patch based Multi-View Stereo (PMVS) Algorithm

Multi-view based reconstruction is always focused in computer graphics and many excellent algorithms have been reported these years. According to Middlebury benchmark, PMVS(Patch based Multi-View Stereo) outperforms all the other submitted algorithms (1). In this paper, we propose an improved PMVS algorithm based on quasi-dense matching to save time cost of the original algorithm. Improved algorithm reduces running time for patch expansion through building a quasi-dense set of initial patches and depresses time complexity of the algorithm. The experiments demonstrate effectiveness of improved algorithm. Keywords-Multi-view reconstruction; PMVS; quasi-dense matching

[1]  Larry S. Davis,et al.  3D Surface Reconstruction Using Graph Cuts with Surface Constraints , 2006, ECCV.

[2]  Roberto Cipolla,et al.  Multi-view stereo via volumetric graph-cuts , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  O. Faugeras,et al.  Variational principles, surface evolution, PDE's, level set methods and the stereo problem , 1998, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[4]  Luc Van Gool,et al.  Combined Depth and Outlier Estimation in Multi-View Stereo , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Jean Ponce,et al.  Carved Visual Hulls for Image-Based Modeling , 2006, International Journal of Computer Vision.

[6]  Derek Bradley,et al.  Accurate multi-view reconstruction using robust binocular stereo and surface meshing , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Long Quan,et al.  A Surface Reconstruction Method Using Global Graph Cut Optimization , 2006, International Journal of Computer Vision.

[8]  Michael Goesele,et al.  Multi-View Stereo Revisited , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Olivier D. Faugeras,et al.  Variational principles, surface evolution, PDEs, level set methods, and the stereo problem , 1998, IEEE Trans. Image Process..

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

[11]  Francis Schmitt,et al.  Silhouette and stereo fusion for 3D object modeling , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[12]  Bruce G. Baumgart,et al.  Geometric modeling for computer vision. , 1974 .

[13]  Long Quan,et al.  A quasi-dense approach to surface reconstruction from uncalibrated images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Philip H. S. Torr,et al.  The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix , 1997, International Journal of Computer Vision.

[15]  Jean Ponce,et al.  Accurate, Dense, and Robust Multiview Stereopsis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[17]  Marc Pollefeys,et al.  Multi-View Stereo via Graph Cuts on the Dual of an Adaptive Tetrahedral Mesh , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[18]  Richard Szeliski,et al.  A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  A. Heyden.  Geometry and algebra of multiple projective transformations , 1995 .

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

[21]  Long Quan,et al.  Match Propagation for Image-Based Modeling and Rendering , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Radu Horaud,et al.  TransforMesh : A Topology-Adaptive Mesh-Based Approach to Surface Evolution , 2007, ACCV.