Direct Digital Surface Model Generation by Semi-Global Vertical Line Locus Matching

As the core issue for Digital Surface Model (DSM) generation, image matching is often implemented in photo space to get disparity or depth map. However, DSM is generated in object space with additional processes such as reference image selection, disparity maps fusion or depth maps merging, and interpolation. This difference between photo space and object space leads to process complexity and computation redundancy. We propose a direct DSM generation approach called the semi-global vertical line locus matching (SGVLL), to generate DSM with dense matching in the object space directly. First, we designed a cost function, robust to the pre-set elevation step and projection distortion, and detected occlusion during cost calculation to achieve a sound photo-consistency measurement. Then, we proposed an improved semi-global cost aggregation with guidance of true-orthophoto to obtain superior results at weak texture regions and slanted planes. The proposed method achieves performance very close to the state-of-the-art with less time consumption, which was experimentally evaluated and verified using nadir aerial images and reference data.

[1]  Yi Guo,et al.  Soft Cost Aggregation with Multi-resolution Fusion , 2014, ECCV.

[2]  Richard Szeliski,et al.  Handling occlusions in dense multi-view stereo , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Li Zhang Automatic Digital Surface Model (DSM) generation from linear array images , 2005 .

[4]  J. Ophir,et al.  Methods for Estimation of Subsample Time Delays of Digitized Echo Signals , 1995 .

[5]  Nan Yang,et al.  A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images , 2016, Remote. Sens..

[6]  Yang Jingyu MVLL Multi-Image Matching Model and Its Application in ADS40 Linear Array Images , 2009 .

[7]  N. Haala The Landscape of Dense Image Matching Algorithms , 2013 .

[8]  Xukun Shen,et al.  PM-PM: PatchMatch With Potts Model for Object Segmentation and Stereo Matching , 2015, IEEE Transactions on Image Processing.

[9]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Marc Pollefeys,et al.  Multi-body Depth-Map Fusion with Non-intersection Constraints , 2014, ECCV.

[11]  Qingxiong Yang,et al.  A non-local cost aggregation method for stereo matching , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[14]  Jesús Angulo,et al.  ( max , min )-convolution and Mathematical Morphology , 2015, ISMM.

[15]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[16]  Changjae Kim,et al.  New Methodologies for True Orthophoto Generation , 2007 .

[17]  J. Ophir,et al.  Methods for estimation of subsample time delays of digitized echo signals. , 1995, Ultrasonic imaging.

[18]  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).

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

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

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

[22]  Stefano Soatto,et al.  Multi-View Stereo Reconstruction of Dense Shape and Complex Appearance , 2005, International Journal of Computer Vision.

[23]  C. Heipke,et al.  STEREOSCOPIC 3 D-IMAGE SEQUENCE ANALYSIS OF SEA SURFACES , 2004 .

[24]  Steven M. Seitz,et al.  Occluding Contours for Multi-view Stereo , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Federico Tombari,et al.  Classification and evaluation of cost aggregation methods for stereo correspondence , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Steven M. Seitz,et al.  Photorealistic Scene Reconstruction by Voxel Coloring , 1997, International Journal of Computer Vision.

[27]  Wilfried Linder,et al.  Digital Photogrammetry: A Practical Course , 2016 .

[28]  Margrit Gelautz,et al.  A layered stereo matching algorithm using image segmentation and global visibility constraints , 2005 .

[29]  Heiko Hirschmüller,et al.  Evaluation of Stereo Matching Costs on Images with Radiometric Differences , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  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).

[31]  C. Fraser,et al.  Accurate and occlusion-robust multi-view stereo , 2015 .

[32]  Carsten Rother,et al.  Fast cost-volume filtering for visual correspondence and beyond , 2011, CVPR 2011.

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

[34]  Long Quan,et al.  Progressive surface reconstruction from images using a local prior , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[35]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[36]  Horst Bischof,et al.  Probabilistic Range Image Integration for DSM and True-Orthophoto Generation , 2013, SCIA.