Archeological excavation monitoring using dense stereo matching techniques

Abstract Several new tools to obtain three-dimensional information from unorganized image sets are now available for the public use. The main advantage of this software, which is based on dense stereo matching, is the possibility to generate 3D content without the need of high-cost hardware (e.g. 3D scanning devices). Nevertheless, their use in real-world application domains (like cultural heritage) is still not very diffused, due to the non-straightforward usability of the raw data produced. In this paper, we investigate the use of automatic dense stereo reconstruction tools for the monitoring of an excavation site. A methodology for the effective acquisition and processing of data is presented. In addition, the results of the data assessment demonstrate the repeatability of the data acquisition process, which is a key factor when qualitative analysis is performed. The use of three-dimensional data is integrated in an open source mesh processing tool, thus showing that a spatio-temporal analysis can be performed in a very intuitive way using off-the-shelf or free/open digital tools. Moreover, the use of peculiar rendering and the creation of snapshots from arbitrary points of view increase the amount of documentation data, and suggest a perfect integration of data produced with dense stereo matching in the future standard documentation for excavation monitoring.

[1]  S. El-Hakim,et al.  SURFACE RECONSTRUCTION OF LARGE COMPLEX STRUCTURES FROM MIXED RANGE DATA – THE ERECHTHEION EXPERIENCE , 2008 .

[2]  Maarten Vergauwen,et al.  Web-based 3D Reconstruction Service , 2006, Machine Vision and Applications.

[3]  Andrea Fusiello,et al.  Structure-and-motion pipeline on a hierarchical cluster tree , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

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

[5]  Francesco Guerra,et al.  NEW INSTRUMENTS FOR SURVEY: ON LINE SOFTWARES FOR 3D RECONTRUCTION FROM IMAGES , 2012 .

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

[7]  育久 満上,et al.  Bundler: Structure from Motion for Unordered Image Collections , 2011 .

[8]  Lars Larsson The Uppåkra Project. Preconditions, Performance and Prospects , 2003 .

[9]  Edward C. Harris,et al.  Techniques of archaeological excavation , 1989 .

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

[11]  Michal Havlena,et al.  Efficient Structure from Motion by Graph Optimization , 2010, ECCV.

[12]  Fabio Remondino,et al.  Image-based Automated Reconstruction of the Great Buddha of Bamiyan, Afghanistan , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

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

[14]  Matthew A. Brown,et al.  Unsupervised 3D object recognition and reconstruction in unordered datasets , 2005, Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05).

[15]  Paolo Cignoni,et al.  Masked photo blending: Mapping dense photographic data set on high-resolution sampled 3D models , 2008, Comput. Graph..

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

[17]  David B. Arnold,et al.  Web based presentation of semantically tagged 3D content for public sculptures and monuments in the UK , 2009, Web3D '09.