Accurate stereo 3D point cloud generation suitable for multi-view stereo reconstruction

This paper proposes a novel methodology for generating 3D point clouds of good accuracy from stereo pairs. Initially, the methodology defines some conditions for the proper selection of image pairs. Then, the selected stereo images are used to estimate dense correspondences using the Daisy descriptor. An efficient two-phase strategy to remove outliers is then introduced. Finally, the 3D point cloud is refined by combining sub-pixel accuracy correspondences estimation and the moving least squares algorithm. The proposed methodology can be exploited by multi-view stereo algorithms due to its good accuracy and its fast computation.

[1]  Andrea Fusiello,et al.  Quasi-Euclidean epipolar rectification of uncalibrated images , 2010, Machine Vision and Applications.

[2]  Olivier D. Faugeras,et al.  Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score , 2007, International Journal of Computer Vision.

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

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

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

[6]  Federico Tombari,et al.  Stereo Vision Enabling Precise Border Localization Within a Scanline Optimization Framework , 2007, ACCV.

[7]  Simon Fuhrmann,et al.  Fusion of depth maps with multiple scales , 2011, ACM Trans. Graph..

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

[9]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[10]  Pascal Fua,et al.  Efficient large-scale multi-view stereo for ultra high-resolution image sets , 2011, Machine Vision and Applications.

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

[12]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Mariette Yvinec,et al.  Surface Reconstruction from Multi-View Stereo of Large-Scale Outdoor Scenes , 2010, Int. J. Virtual Real..

[14]  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.

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

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

[17]  Limin Luo,et al.  Dense Stereo Correspondence with Contrast Context Histogram, Segmentation-Based Two-Pass Aggregation and Occlusion Handling , 2009, PSIVT.

[18]  Federico Tombari,et al.  Segmentation-Based Adaptive Support for Accurate Stereo Correspondence , 2007, PSIVT.

[19]  FusielloAndrea,et al.  Quasi-Euclidean epipolar rectification of uncalibrated images , 2011, MVA 2011.

[20]  D. Levin,et al.  Mesh-Independent Surface Interpolation , 2004 .