City-Scale Modeling for Street Navigation

This paper proposes a semi-automatic image-based approach for 3-dimensional (3D) modeling of buildings along streets. Image-based urban 3D modeling techniques are typically based on the use of aerial and ground-level images. The aerial image of the relevant area is extracted from publically available sources in Google Maps by stitching together different patches of the map. Panoramic images are common for ground-level recording because they have advantages for 3D modeling. A panoramic video recorder is used in the proposed approach for recording sequences of ground-level spherical panoramic images. The proposed approach has two advantages. First, detected camera trajectories are more accurate and stable (compared to methods using multi-view planar images only) due to the use of spherical panoramic images. Second, we extract the texture of a facade of a building from a single panoramic image. Thus, there is no need to deal with color blending problems that typically occur when using overlapping textures.

[1]  George Wolberg,et al.  Multiview Geometry for Texture Mapping 2D Images Onto 3D Range Data , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Lu Wang,et al.  Semiautomatic registration between ground-level panoramas and an orthorectified aerial image for building modeling , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[3]  Richard Szeliski,et al.  Seamless Stitching using Multi-Perspective Plane Sweep , 2004 .

[4]  Ioannis Stamos,et al.  Geometry and Texture Recovery of Scenes of Large Scale , 2002, Comput. Vis. Image Underst..

[5]  C. Brenner.  FAST PRODUCTION OF VIRTUAL REALITY CITY MODELS , 2003 .

[6]  Jan-Michael Frahm,et al.  Detailed Real-Time Urban 3D Reconstruction from Video , 2007, International Journal of Computer Vision.

[7]  ARMIN GRUEN.  Automation in Building Reconstruction , 2000 .

[8]  Christian Früh,et al.  Constructing 3D City Models by Merging Aerial and Ground Views , 2003, IEEE Computer Graphics and Applications.

[9]  Jana Kosecka,et al.  Piecewise planar city 3D modeling from street view panoramic sequences , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Frederic Devernay,et al.  Using robust methods for automatic extraction of buildings , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  Hans-Gerd Maas,et al.  The suitability of airborne laser scanner data for automatic 3D object reconstruction , 2001 .

[12]  Christian Früh,et al.  An Automated Method for Large-Scale, Ground-Based City Model Acquisition , 2004, International Journal of Computer Vision.

[13]  Suya You,et al.  Automatic Pose Recovery for High-Quality Textures Generation , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[14]  Mei Han,et al.  Interactive construction of 3D models from panoramic mosaics , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[15]  Reinhard Klette,et al.  Panoramic Imaging: Sensor-Line Cameras and Laser Range-Finders , 2008 .

[16]  Nam Ho Kim,et al.  HFAT: Log-Based FAT File System Using Dynamic Allocation Method , 2012, J. Inform. and Commun. Convergence Engineering.

[17]  Christian Früh,et al.  3D model generation for cities using aerial photographs and ground level laser scans , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.