Least-squares building model fitting using aerial photos and LiDAR data

Building models are conventionally reconstructed by measuring their vertices point-by-point in a digital photogrammetric workstation (DPW), which is time and labor consuming process. Although aerial photos implicitly provide 3D information of buildings, LiDAR systems directly provide high density and accurate point cl oud coordinates. However, LiDAR data cannot accurately r present the building boundaries. To take advantag e of both systems, we propose Floating Model and a tailored least-squares model-data fitting (LSMDF) algorithm in this paper. The floating model is a pre-defined primitiv e model, which is described by a set of parameters, floating in the space. A building is reconstructed by adjusting the se model parameters so the wire-frame model adequat ly fits the building’s boundary in all overlapping photos and L iDAR data. The semi-automated modeling procedure co nsists of 3 steps. First, the operator chooses an appropriate model and approximately fit it to the building’s o utlines on the aerial photos. Then, an automated procedure compute s the optimal fit between the models and both of ae rial photos and LiDAR data using an iterative LSMDF algorithm. Finally, the model parameters and standard deviatio ns are provided, and the wire-frame model is superimposed on all overlapping aerial photos for the operator t o check or modify the results. To test the proposed algorithm and approach, an image block of 4 panchromatic aeri al photos and corresponding LiDAR data are selected for the e xperiments. The ground resolution of the image is approximately 5cm. The point density of LiDAR point cloud is about 4 -5point/m. The reconstructed models are manually evaluated and compared. Most of the buildi ngs are accurately modeled, and the fitting result achieves the photogrammetric accuracy. In addition, the implicit onstraints within the model, such as the parallel edges or rectangle corners, will keep the building shape wit hout distortion.

[1]  Pierre ERMES.  CONSTRAINTS IN CAD MODELS FOR REVERSE ENGINEERING USING PHOTOGRAMMETRY , 2010 .

[2]  George Vosselman,et al.  Reconstruction of 3D building models from aerial images and maps , 2003 .

[3]  Changjae Kim,et al.  New Methodologies for True Orthophoto Generation , 2007 .

[4]  Xinhua Wang,et al.  Generation and visualization of 3D-city and facility models using CyberCity Modeler (CC-Modeler™) , 2003 .

[5]  Dieter Fritsch,et al.  3D-City Models: Automatic and Semiautomatic Acquisition Methods , 1999 .

[6]  C. Brenner.  Building reconstruction from images and laser scanning , 2005 .

[7]  Henri Veldhuis.  PERFORMANCE ANALYSIS OF TWO FITTING ALGORITHMS FOR THE MEASUREMENT OF PARAMETERISED OBJECTS , 1998 .

[8]  E. Baltsavias,et al.  3-D Building Reconstruction with ARUBA: A Qualitative and Quantitative Evaluation , 1997 .

[9]  Babak Ameri,et al.  FEATURE BASED MODEL VERIFICATION (FBMV): A NEW CONCEPT FOR HYPOTHESIS VALIDATION IN BUILDING RECONSTRUCTION , 2000 .

[10]  Bir Bhanu,et al.  Generic object recognition using multiple representations , 1996, Image Vis. Comput..

[11]  Dieter Fritsch,et al.  Interactive modelling tools for 3D building reconstruction , 1999 .

[12]  Azriel Rosenfeld,et al.  Guest Editorial Introduction To The Special Issue On Automatic Target Detection And Recognition , 1997, IEEE Trans. Image Process..

[13]  Sendo Wang,et al.  Least‐squares model‐image fitting of floating models for building extraction from images , 2009 .

[14]  F. Landstorfer,et al.  Dominant Path Prediction Model for Urban Scenarios , 2005 .

[15]  Claus Brenner.  Interactive modelling tools for 3D building reconstruction , 2000 .

[16]  Timo Ropinski,et al.  A HYBRID DECISION SUPPORT SYSTEM FOR 3D CITY PLANNING , 2006 .

[17]  F. A. Heuvel,et al.  TRENDS IN CAD-BASED PHOTOGRAMMETRIC MEASUREMENT , 2000 .

[18]  Su-Shing Chen,et al.  Three-Dimensional Object Recognition Using Range Data , 1989, Other Conferences.

[19]  P. Litkey,et al.  INTEGRATION OF LASER SCANNING AND PHOTOGRAMMETRY , 2007 .

[20]  George Vosselman,et al.  Mapping by Dragging and Fitting of Wire-Frame Models , 1999 .

[21]  Ismo Rakkolainen,et al.  A 3D City Info for mobile users , 2001, Comput. Graph..

[22]  Kurt Kubik,et al.  Automatic Building Detection Using the Dempster-Shafer Algorithm , 2006 .

[23]  Roman Englert,et al.  One-Eye Stereo System for the Acquisition of Complex 3D Building Descriptions , 1996 .

[24]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..

[25]  Claus Brenner.  TOWARDS FULLY AUTOMATIC GENERATION OF CITY MODELS , 2000 .

[26]  Wolfgang Förstner,et al.  Object Location Based on Uncertain Models , 1989, DAGM-Symposium.

[27]  Yi-Hsing Tseng,et al.  SEMIAUTOMATED BUILDING EXTRACTION BASED ON CSG MODEL-IMAGE FITTING , 2003 .

[28]  Andrew Zisserman,et al.  Automatic reconstruction of piecewise planar models from multiple views , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[29]  Wolfgang Förstner,et al.  Models for photogrammetric building reconstruction , 1995, Comput. Graph..

[30]  Changjae Kim,et al.  Object-Based Integration of Photogrammetric and LiDAR Data for Automated Generation of Complex Polyhedral Building Models , 2009, Sensors.

[31]  Yi-Hsing Tseng,et al.  ON THE ACCURACY ASSESSMENT OF LEAST-SQUARES MODEL-IMAGE FITTING FOR BUILDING EXTRACTION FROM AERIAL IMAGES , 2001 .

[32]  E. Baltsavias,et al.  Automatic Extraction of Man-Made Objects from Aerial and Space Images (II) , 1995 .

[33]  Fiona Lang,et al.  3D-city modeling with a digital one-eye stereo system , 1996 .

[34]  Jan Claus Brenner.  Automated Extraction of Features from CAD Models for 3D Object Recognition , 2000 .

[35]  A. Grün,et al.  Semi-automated approaches to site recording and modeling , 2000 .