GRAPHOS – open‐source software for photogrammetric applications

This paper reports the latest developments for the photogrammetric opensource tool called GRAPHOS (inteGRAted PHOtogrammetric Suite). GRAPHOS includes some recent innovations in the image-based 3D reconstruction pipeline, from automatic feature detection/description and network orientation to dense image matching and quality control. GRAPHOS also has a strong educational component beyond its automated processing functions, reinforced with tutorials and didactic explanations about algorithms and performance. The paper highlights recent developments carried out at different levels: graphical user interface (GUI), didactic simulators for image processing, photogrammetric processing with weight parameters, dataset creation and system evaluation.

[1]  M. Havlena,et al.  Recent developments in large-scale tie-point matching , 2016 .

[2]  Steven M. Seitz,et al.  Multicore bundle adjustment , 2011, CVPR 2011.

[3]  Norbert Pfeifer,et al.  Mind your grey tones : examining the influence of decolourization methods on interest point extraction and matching for architectural image-based modelling , 2015 .

[4]  Simon Fuhrmann,et al.  MVE - An image-based reconstruction environment , 2015, Comput. Graph..

[5]  K. Wallis Seasonal Adjustment and Relations Between Variables , 1974 .

[6]  C. Fraser,et al.  Digital camera calibration methods: Considerations and comparisons , 2006 .

[7]  Fabrizio Ivan Apollonio,et al.  Evaluation of feature-based methods for automated network orientation , 2014 .

[8]  M. Pierrot-Deseilligny,et al.  A MULTIRESOLUTION AND OPTIMIZATION-BASED IMAGE MATCHING APPROACH : AN APPLICATION TO SURFACE RECONSTRUCTION FROM SPOT 5-HRS STEREO IMAGERY , 2006 .

[9]  M. Gaiani,et al.  Intensity histogram equalisation , a colour-to-grey conversion strategy improving photogrammetric reconstruction of urban architectural heritage , 2016 .

[10]  H. M. Karara,et al.  Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry , 2015 .

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

[12]  M. Pierrot Deseilligny,et al.  APERO, AN OPEN SOURCE BUNDLE ADJUSMENT SOFTWARE FOR AUTOMATIC CALIBRATION AND ORIENTATION OF SET OF IMAGES , 2012 .

[13]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Diego González Aguilera,et al.  SV3DVISION: DIDACTICAL PHOTOGRAMMETRIC SOFTWARE FOR SINGLE IMAGE-BASED MODELING , 2006 .

[15]  Marc Pierrot Deseilligny,et al.  Protocols and Assisted Tools for Effective Image-Based Modeling of Architectural Elements , 2012, EuroMed.

[16]  Joseph O'Rourke,et al.  Computational Geometry in C. , 1995 .

[17]  M. Rothermel,et al.  SURE : PHOTOGRAMMETRIC SURFACE RECONSTRUCTION FROM IMAGER Y , 2013 .

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

[19]  Cewu Lu,et al.  Contrast preserving decolorization , 2012, 2012 IEEE International Conference on Computational Photography (ICCP).

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

[21]  Andrew Owens,et al.  Discrete-continuous optimization for large-scale structure from motion , 2011, CVPR 2011.

[22]  S. El-Hakim Real-time image metrology with CCD cameras , 1986 .

[23]  Dario Maio,et al.  Saliency-based keypoint selection for fast object detection and matching , 2015, Pattern Recognit. Lett..

[24]  Joachim Höhle The Assessment of the Absolute Planimetric Accuracy of Airborne Laserscanning , 2011 .

[25]  Torsten Sattler,et al.  A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Josef Jansa,et al.  Advanced methods and applications , 1997 .

[27]  ARNO KNAPITSCH,et al.  Tanks and temples , 2017, ACM Trans. Graph..

[28]  José Luis Lerma,et al.  Virtual Worlds for Photogrammetric Image-Based Simulation and Learning , 2013 .

[29]  Federico Tombari,et al.  Interest Points via Maximal Self-Dissimilarities , 2014, ACCV.

[30]  Fabio Remondino,et al.  State of the art in high density image matching , 2014 .

[31]  Zuzana Kukelova,et al.  A minimal solution to the autocalibration of radial distortion , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Pierre Grussenmeyer,et al.  Possibilities and limits of web photogrammetry : experiences with the ARPENTEUR web based tool , 2001 .

[33]  Himanshu Aggarwal,et al.  A Comprehensive Review of Image Enhancement Techniques , 2010, ArXiv.

[34]  David G. Lowe,et al.  Local feature view clustering for 3D object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[35]  Thomas Luhmann,et al.  Learning Photogrammetry with Interactive Software Tool PhoX , 2016 .

[36]  R. Binet,et al.  Measurement of ground displacement from optical satellite image correlation using the free open-source software MicMac , 2015 .

[37]  Torsten Sattler,et al.  Comparative Evaluation of Hand-Crafted and Learned Local Features , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Diego González-Aguilera,et al.  Confronting Passive and Active Sensors with Non-Gaussian Statistics , 2014, Sensors.

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

[40]  Kostas Daniilidis,et al.  Fully Automatic Registration of 3D Point Clouds , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[41]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[42]  Michael Goesele,et al.  Let There Be Color! Large-Scale Texturing of 3D Reconstructions , 2014, ECCV.

[43]  Diego González-Aguilera,et al.  Accuracy assessment of airborne laser scanner dataset by means of parametric and non-parametric statistical methods , 2015 .

[44]  Diego González-Aguilera,et al.  Development of an All-Purpose Free Photogrammetric Tool , 2016 .

[45]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[46]  Jan-Michael Frahm,et al.  Reconstructing the world* in six days , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  Jan-Michael Frahm,et al.  Structure-from-Motion Revisited , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[48]  Richard Szeliski,et al.  Bundle Adjustment in the Large , 2010, ECCV.

[49]  Fabrizio Ivan Apollonio,et al.  An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes , 2016, Remote. Sens..

[50]  Jean-Michel Morel,et al.  ASIFT: A New Framework for Fully Affine Invariant Image Comparison , 2009, SIAM J. Imaging Sci..

[51]  Jan-Michael Frahm,et al.  Pixelwise View Selection for Unstructured Multi-View Stereo , 2016, ECCV.