Scalable multi-view stereo

This paper presents a scalable multi-view stereo reconstruction method which can deal with a large number of large unorganized images in affordable time and effort. The computational effort of our technique is a linear function of the surface area of the observed scene which is conveniently discretized to represent sufficient but not excessive detail. Our technique works as a filter on a limited number of images at a time and can thus process arbitrarily large data sets using limited memory. By building reconstructions gradually, we avoid unnecessary processing of data which bring little improvement. In experiments with Middlebury and Strecha's databases, we demonstrate that we achieve results comparable to the state of the art with considerably smaller effort than used by previous methods. We present a large scale experiments in which we processed 294 unorganized images of an outdoor scene and reconstruct its 3D model and 1000 images from the Google Street View Pittsburgh Experimental Data Set 1.

[1]  Roberto Cipolla,et al.  Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo , 2008, ECCV.

[2]  Tomás Pajdla,et al.  Robust Rotation and Translation Estimation in Multiview Reconstruction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Roberto Cipolla,et al.  Probabilistic visibility for multi-view stereo , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

[6]  Leif Kobbelt,et al.  Hierarchical Volumetric Multi-view Stereo Reconstruction of Manifold Surfaces based on Dual Graph Embedding , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Radim Sára,et al.  Efficient Sampling of Disparity Space for Fast And Accurate Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Michael Goesele,et al.  Multi-View Stereo for Community Photo Collections , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[9]  Martin J. Wainwright,et al.  MAP estimation via agreement on (hyper)trees: Message-passing and linear programming , 2005, ArXiv.

[10]  Randal C. Burns,et al.  Multilevel streaming for out-of-core surface reconstruction , 2007, Symposium on Geometry Processing.

[11]  Michael M. Kazhdan,et al.  Poisson surface reconstruction , 2006, SGP '06.

[12]  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..

[13]  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..

[14]  I. Reid,et al.  Fast Feature Detection with a Graphics Processing Unit Implementation , 2006 .

[15]  Jan-Michael Frahm,et al.  Real-Time Visibility-Based Fusion of Depth Maps , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[16]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[17]  Jean-Philippe Pons,et al.  Efficient Multi-View Reconstruction of Large-Scale Scenes using Interest Points, Delaunay Triangulation and Graph Cuts , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[18]  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.

[19]  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).

[20]  Horst Bischof,et al.  A Globally Optimal Algorithm for Robust TV-L1 Range Image Integration , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[21]  James H. Clark,et al.  Hierarchical geometric models for visible surface algorithms , 1976, CACM.

[22]  Leif Kobbelt,et al.  Image selection for improved Multi-View Stereo , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Michal Havlena,et al.  Measuring camera translation by the dominant apical angle , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Martin J. Wainwright,et al.  MAP estimation via agreement on trees: message-passing and linear programming , 2005, IEEE Transactions on Information Theory.

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

[26]  T. Pajdla,et al.  Segmentation based Multi-View Stereo , 2009 .

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

[28]  Luc Van Gool,et al.  Wide-baseline stereo from multiple views: A probabilistic account , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[29]  Yuri Boykov,et al.  A Scalable graph-cut algorithm for N-D grids , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Walter G. Kropatsch,et al.  Depth Map Fusion with Camera Position Refinement , 2009 .

[31]  Vladimir Kolmogorov,et al.  Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[33]  Pascal Fua,et al.  On benchmarking camera calibration and multi-view stereo for high resolution imagery , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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