Toward automatic 3D modeling of scenes using a generic camera model

The automatic reconstruction of 3D models from image sequences is still a very active field of research. All existing methods are designed for a given camera model, and a new (and ambitious) challenge is 3D modeling with a method which is exploitable for any kind of camera. A similar approach was recently suggested for structure-from-motion thanks to the use of generic camera models. In this paper, we first introduce geometric tools designed for 3D scene modeling with a generic camera model. Then, these tools are used to solve many issues: matching errors, wide range of point depths, depth discontinuities, and view-point selection for reconstruction. Experiments are provided for perspective and catadioptric cameras.

[1]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[2]  Reinhard Koch,et al.  Visual Modeling with a Hand-Held Camera , 2004, International Journal of Computer Vision.

[3]  Maxime Lhuillier Toward Flexible 3D Modeling using a Catadioptric Camera , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Long Quan,et al.  A quasi-dense approach to surface reconstruction from uncalibrated images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Juho Kannala,et al.  A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  David Nistér,et al.  A Minimal Solution to the Generalised 3-Point Pose Problem , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  Michel Dhome,et al.  Generic and Real-Time Structure from Motion , 2007, BMVC.

[8]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[9]  Konrad Schindler,et al.  On Robust Regression in Photogrammetric Point Clouds , 2003, DAGM-Symposium.

[10]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Ben J. A. Kröse,et al.  Robust scene reconstruction from an omnidirectional vision system , 2003, IEEE Trans. Robotics Autom..

[12]  Peter F. Sturm,et al.  A generic structure-from-motion framework , 2006, Comput. Vis. Image Underst..

[13]  David Nister,et al.  Automatic Dense Reconstruction from Uncalibrated Video Sequences , 2001 .

[14]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[15]  Robert Pless,et al.  Using many cameras as one , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[16]  Jan-Michael Frahm,et al.  Towards Urban 3D Reconstruction from Video , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[17]  Tomáš Svoboda,et al.  Reliable 3D reconstruction from a few catadioptric images , 2002, Proceedings of the IEEE Workshop on Omnidirectional Vision 2002. Held in conjunction with ECCV'02.

[18]  Shree K. Nayar,et al.  A general imaging model and a method for finding its parameters , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.