VALIDATING PHOTOGRAMMETRIC ORIENTATION STEPS BY THE USE OF RELEVANT THEORETICAL MODELS. IMPLEMENTATION IN THE "ARPENTEUR" FRAMEWORK

The new advance in photogrammetry using the automatic procedures such as the famous algorithm which was proposed by David Lowe (Lowe, 2004) features descriptors and matching (SIFT) and then the recent development of external orientation (Nister (Stewenius et alii, 2006) or Snavely (Snavely et alii, 2010)) have changed drastically the way of measuring space with photogrammetry. The complexity of the process and the huge quantity of processed data (thousands of photographs) makes difficult validating the different process steps. We propose in this paper several theoretical model generation methods in order to validate the complete photogrammetric orientation process. A theoretical photogrammetric model generation has been developed in order to produce photographs, photo orientation, 3D points and 2D observations according to some defined camera and a parametric photograph distribution in the scene. In addition the use of synthesis image software generation as POV-Ray allow us to generate set of photographs with pre-computed internal and external orientation in order to check the whole pipeline from feature extraction to Photographs External Orientation. We apply this model generation approach to several typical geometry of photogrammetric scene, stereo, parallel triplet, parallel strip and convergent models.

[1]  Andrea Fusiello,et al.  Quasi-Euclidean epipolar rectification of uncalibrated images , 2010, Machine Vision and Applications.

[2]  Gamal H. Seedahmed,et al.  Direct retrieval of exterior orientation parameters using a 2D projective transformation , 2006 .

[3]  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..

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

[5]  Pierre Drap,et al.  Performances Analysis of Underwater Image Preprocessing Techniques on the Repeatability of SIFT and SURF Descriptors , 2012 .

[6]  David Nister,et al.  Recent developments on direct relative orientation , 2006 .

[7]  Nicolas Paparoditis,et al.  The Five Points Pose Problem : A New and Accurate Solution Adapted to any Geometric Configuration , 2008, PSIVT.

[8]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[9]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[10]  Andrew Zisserman,et al.  Multiple View Geometry , 1999 .

[11]  Christian Heipke,et al.  Automation of interior, relative, and absolute orientation , 1997 .

[12]  Dean Brown,et al.  Decentering distortion of lenses , 1966 .

[13]  Michael Goesele,et al.  Scene Reconstruction and Visualization From Community Photo Collections , 2010, Proceedings of the IEEE.

[14]  M. Lo Brutto,et al.  Computer Vision Tools for 3D Modelling in Archaeology , 2012 .

[15]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[16]  Manolis I. A. Lourakis,et al.  Is Levenberg-Marquardt the most efficient optimization algorithm for implementing bundle adjustment? , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[17]  Manolis I. A. Lourakis,et al.  SBA: A software package for generic sparse bundle adjustment , 2009, TOMS.

[18]  Juha Hyyppä,et al.  Determination of Exterior Orientation Using Linear Features from Vector Maps , 2006 .

[19]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.