Mesh Optimisation Using Edge Information in Feature-Based Surface Reconstruction

One of the most challenging and fundamental problems in computer vision is to reconstruct a surface model given a set of uncalibrated 2D images. Well-established Structure from Motion (SfM) algorithms often result in a sparse set of 3D surface points, but surface modelling based on sparse 3D points is not easy. In this paper, we present a new method to refine and optimise surface meshes using edge information in the 2D images. We design a meshing – edge point detection – re-meshing scheme that can gradually refine the surface mesh until it best fits the true physical surface of the object being modelled. Our method is tested on real images and satisfactory results are obtained.

[1]  Takeo Kanade,et al.  Image-consistent surface triangulation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Stanley Osher,et al.  Implicit and Nonparametric Shape Reconstruction from Unorganized Data Using a Variational Level Set Method , 2000, Comput. Vis. Image Underst..

[4]  Kenichi Kanatani,et al.  Mesh optimization using an inconsistency detection template , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

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

[7]  Jun Liu,et al.  Automatic Camera Calibration and Scene Reconstruction with Scale-Invariant Features , 2006, ISVC.

[8]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[9]  Shiri Gordon,et al.  An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  A. Heyden,et al.  Reconstructing open surfaces from unorganized data points , 2004, CVPR 2004.

[11]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[12]  Tony DeRose,et al.  Surface reconstruction from unorganized points , 1992, SIGGRAPH.

[13]  Richard Szeliski,et al.  Modeling surfaces of arbitrary topology with dynamic particles , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Long Quan,et al.  Surface reconstruction by integrating 3D and 2D data of multiple views , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  Roberto Cipolla,et al.  Bayesian Stochastic Mesh Optimization for 3D reconstruction , 2003, BMVC.

[16]  Reinhard Koch,et al.  Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[17]  Bill Triggs,et al.  Autocalibration and the absolute quadric , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Camillo J. Taylor Surface reconstruction from feature based stereo , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[19]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.

[20]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[21]  Long Quan,et al.  A Surface Reconstruction Method Using Global Graph Cut Optimization , 2006, International Journal of Computer Vision.

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