A Photogrammetric and Computer Vision-Based Approach for Automated 3D Architectural Modeling and Its Typological Analysis

Thanks to the advances in integrating photogrammetry and computer vision, as well as in some numeric algorithms and methods, it is possible to aspire to turn 2D (images) into 3D (point clouds) in an automatic, flexible and good-quality way. This article presents a new method through the development of PW (Photogrammetry Workbench) (and how this could be useful for architectural modeling). This tool enables the user to turn images into scale 3D point cloud models, which have a better quality than those of laser systems. Moreover, the point clouds may include the respective orthophotos with photographic texture. The method allows the study of the typology of architecture and has been successfully tested on a sample of ten religious buildings located in the region of Aliste, Zamora (Spain).

[1]  Duane C. Brown,et al.  Close-Range Camera Calibration , 1971 .

[2]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[3]  Robin Letellier,et al.  Recording, Documentation and Information Management for the Conservation of Heritage Places , 2015 .

[4]  S. Gedam,et al.  Area Based Image Matching Methods – A Survey , 2012 .

[5]  Myron L. Braunstein,et al.  Structure from Motion , 2015, The Encyclopedia of Archaeological Sciences.

[6]  Petros Patias,et al.  Introduction to Heritage Documentation , 2011 .

[7]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.

[9]  Henrique Lorenzo,et al.  A methodology for rapid archaeological site documentation using ground‐penetrating radar and terrestrial photogrammetry , 2005 .

[10]  Henrique Lorenzo,et al.  CLOSE RANGE DIGITAL PHOTOGRAMMETRY AND SOFTWARE APPLICATION DEVELOPMENT FOR PLANAR PATTERNS COMPUTATION FOTOGRAMETRÍA TERRESTRE DIGITAL Y APLICACIÓN SOFTWARE PARA EL DESARROLLO DE PATRONES PLANOS , 2009 .

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

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

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

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

[15]  Lionel Moisan,et al.  A Probabilistic Criterion to Detect Rigid Point Matches Between Two Images and Estimate the Fundamental Matrix , 2004, International Journal of Computer Vision.

[16]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[17]  Konrad Schindler,et al.  An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Henrique Lorenzo,et al.  3D Modeling and Section Properties of Ancient Irregular Timber Structures by Means of Digital Photogrammetry , 2007, Comput. Aided Civ. Infrastructure Eng..

[19]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[21]  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).

[22]  Xian-Yong Liu,et al.  High-accuracy three-dimensional shape acquisition of a large-scale object from multiple uncalibrated camera views. , 2011, Applied optics.

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

[24]  Berthold K. P. Horn Recovering Baseline and Orientation from Essential Matrix , 1990 .

[25]  Leif Kobbelt,et al.  Iterative multi - view plane fitting , 2006 .

[26]  Ping Tan Image-Based Modeling , 2014, Computer Vision, A Reference Guide.

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

[28]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[29]  A. Gruen ADAPTIVE LEAST SQUARES CORRELATION: A POWERFUL IMAGE MATCHING TECHNIQUE , 1985 .

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

[31]  Stuart Robson,et al.  Close Range Photogrammetry: Principles, Methods and Applications , 2006 .

[32]  N. Haala,et al.  Mobile LiDAR mapping for 3D point cloud collecation in urban areas : a performance test , 2008 .