Automated 3D Structure Inference of Civil Infrastructure Using a Stereo Camera Set

The commercial far-range (>10m) infrastructure spatial data collection methods are not completely automated. They need significant amount of manual post-processing work and in some cases, the equipment costs are significant. This paper presents a method that is the first step of a stereo videogrammetric framework and holds the promise to address these issues. Under this method, video streams are initially collected from a calibrated set of two video cameras. For each pair of simultaneous video frames, visual feature points are detected and their spatial coordinates are then computed. The result, in the form of a sparse 3D point cloud, is the basis for the next steps in the framework (i.e., camera motion estimation and dense 3D reconstruction). A set of data, collected from an ongoing infrastructure project, is used to show the merits of the method. Comparison with existing tools is also shown, to indicate the performance differences of the proposed method in the level of automation and the accuracy of results.

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

[2]  Silvio Savarese,et al.  Application of D4AR - A 4-Dimensional augmented reality model for automating construction progress monitoring data collection, processing and communication , 2009, J. Inf. Technol. Constr..

[3]  Niko Sünderhauf,et al.  COMPARING SEVERAL IMPLEMENTATIONS OF TWO RECENTLY PUBLISHED FEATURE DETECTORS , 2007 .

[4]  Richard I. Hartley,et al.  In Defense of the Eight-Point Algorithm , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

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

[6]  Ioannis Brilakis,et al.  Comparison of Optical Sensor-Based Spatial Data Collection Techniques for Civil Infrastructure Modeling , 2009 .

[7]  Richard I. Hartley,et al.  In defence of the 8-point algorithm , 1995, Proceedings of IEEE International Conference on Computer Vision.

[8]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[9]  Zubair Ahmed Memon,et al.  An automatic project progress monitoring model by integrating autocad and digital photos , 2005 .

[10]  Hyojoo Son,et al.  3D structural component recognition and modeling method using color and 3D data for construction progress monitoring , 2010 .

[11]  Kenichi Kanatani,et al.  Statistical optimization for geometric computation - theory and practice , 1996, Machine intelligence and pattern recognition.

[12]  Burcu Akinci,et al.  A formalism for utilization of sensor systems and integrated project models for active construction quality control , 2006 .

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

[14]  Fei Dai,et al.  Assessing the Accuracy of Applying Photogrammetry to Take Geometric Measurements on Building Products , 2010 .

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

[16]  Burcu Akinci,et al.  Quantification of edge loss of laser scanned data at spatial discontinuities , 2009 .

[17]  Changwan Kim,et al.  Rapid, on-site spatial information acquisition and its use for infrastructure operation and maintenance , 2005 .

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