3D Reconstruction of Archaeological Trenches from Photographs

This paper presents a method for 3D reconstructions of archaeological excavation sites. The method extends a 3D reconstruction algorithm for general rigid scenes to better fit the special archaeological needs and to integrate easily into the documentation process. As input, an ordered image sequence captured with a calibrated standard digital camera is required, along with a small set of 3D points from the trench with well-known coordinates. The 3D points are used to transform the model into the world coordinate system used at the excavation site, so measuring in the model and fusing it with other models becomes possible. Furthermore, a new algorithm called LoopClosing is introduced to minimize drift and increase accuracy. The resulting models provide lasting 3D representations of the trenches and allow the user to explore the scene interactively, not being restricted to a photographer’s point of view. True orthographic views can be generated from the 3D models that can be correlated with other archaeological data.

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

[2]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  Luc Van Gool,et al.  3D MURALE: a multimedia system for archaeology , 2001, VAST '01.

[4]  M. Ioannides,et al.  3D-RECONSTRUCTION & RE-PRODUCTION IN ARCHAEOLOGY , 2004 .

[5]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Fabio Remondino,et al.  3D Virtual reconstruction and visualization of complex architectures: The 3D-ARCH project , 2009 .

[7]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[8]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[9]  V. TSIOUKAS,et al.  A NOVEL SYSTEM FOR THE 3 D RECONSTRUCTION OF SMALL ARCHAEOLOGICAL OBJECTS , 2004 .

[10]  Alexandru Tupan,et al.  Triangulation , 1997, Comput. Vis. Image Underst..

[11]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[13]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[14]  Larry S. Davis,et al.  Model-based object pose in 25 lines of code , 1992, International Journal of Computer Vision.

[15]  AUTOMATIC RECONSTRUCTION FOR SMALL ARCHEOLOGY BASED ON CLOSE-RANGE PHOTOGRAMMETRY , 2008 .

[16]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[17]  R. Hartley Triangulation, Computer Vision and Image Understanding , 1997 .

[18]  Reinhard Koch,et al.  Visual Modeling with a Hand-Held Camera , 2004, International Journal of Computer Vision.

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

[20]  Gabriele Bitelli F. Remondino, S. El-Hakim, S. Girardi, A. Rizzi, S. Benedetti, L. Gonzo: "3D Virtual reconstruction and visualization of complex architectures" , 2013 .

[21]  Maarten Vergauwen,et al.  3D acquisition of archaeological heritage from images , 2001 .