Hough-Transform and Extended RANSAC Algorithms for Automatic Detection of 3D Building Roof Planes from Lidar Data

Airborne laser scanner technique is broadly the most appropriate way to acquire rapidly and with high density 3D data over a city. Once the 3D Lidar data are available, the next task is the automatic data processing, with major aim to construct 3D building models. Among the numerous automatic reconstruction methods, the techniques allowing the detection of 3D building roof planes are of crucial importance. Three main methods arise from the literature: region growing, Hough-transform and Random Sample Consensus (RANSAC) paradigm. Since region growing algorithms are sometimes not very transparent and not homogenously applied, this paper focuses only on the Hough-transform and the RANSAC algorithm. Their principles, their pseudocode - rarely detailed in the related literature - as well as their complete analyses are presented in this paper. An analytic comparison of both algorithms, in terms of processing time and sensitivity to cloud characteristics, shows that despite the limitation encountered in both methods, RANSAC algorithm is still more efficient than the first one. Under other advantages, its processing time is negligible even when the input data size is very large. On the other hand, Hough-transform is very sensitive to the segmentation parameters values. Therefore, RANSAC algorithm has been chosen and extended to exceed its limitations. Its major limitation is that it searches to detect the best mathematical plane among 3D building point cloud even if this plane does not always represent a roof plane. So the proposed extension allows harmonizing the mathematical aspect of the algorithm with the geometry of a roof. At last, it is shown that the extended approach provides very satisfying results, even in the case of very weak point density and for different levels of building complexity. Therefore, once the roof planes are successfully detected, the automatic building modelling can be carried out.

[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]  George Vosselman,et al.  Two algorithms for extracting building models from raw laser altimetry data , 1999 .

[3]  D. Fritsch,et al.  AUTOMATIC 3D BUILDING RECONSTRUCTION USING PLANE-ROOF STRUCTURES , 2000 .

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

[5]  George Vosselman,et al.  3D BUILDING MODEL RECONSTRUCTION FROM POINT CLOUDS AND GROUND PLANS , 2001 .

[6]  Primo Zingaretti,et al.  Building Reconstruction and Visualization from LIDAR Data , 2003 .

[7]  Franz Rottensteiner,et al.  Automatic Generation of High-Quality Building Models from Lidar Data , 2003, IEEE Computer Graphics and Applications.

[8]  E. Kjems,et al.  Automatic 3D building reconstruction from airbornelaser scanning and cadastral data using hough transform , 2004 .

[9]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[10]  R. Siegwart,et al.  A comparison of line extraction algorithms using 2D laser rangefinder for indoor mobile robotics , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Primo Zingaretti,et al.  Complete classification of raw LIDAR data and 3D reconstruction of buildings , 2006, Pattern Analysis and Applications.

[12]  T. Rabbani,et al.  EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS , 2005 .

[13]  E. Mikhail,et al.  Manual of Photogrammetry, 5th Edition , 2006 .

[14]  P. Grussenmeyer,et al.  Joint combination of point cloud and DSM for 3D building reconstruction using airborne laser scanner data , 2007, 2007 Urban Remote Sensing Joint Event.