Complete 3D model reconstruction using a depth sensor

We propose a fast and simple application system of 3D model reconstruction. We acquire range images by using a combination of a regular camera and a depth sensor. The reconstruction of a 3D model consists of four key steps: (i) Initial alignment either feature tracking or the 4-points congruent sets algorithm is used to align surfaces captured at different frames. (ii)The iterative closest point (ICP) method is applied to further align the piecewise surfaces from the last step. (iii) The surfaces are merged into a whole 3D model by the volumetric method. (iv) In the refinement step, we fill holes and produce a complete 3D model that approximates the original model with robust repair of polygonal models. At last, we present the experimental results which show that the errors between our reconstructed model and the ground truth are less than 1%.

[1]  Chitra Dorai,et al.  Optimal Registration of Object Views Using Range Data , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Richard Szeliski,et al.  A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Ruigang Yang,et al.  Fusion of time-of-flight depth and stereo for high accuracy depth maps , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Daniel Cohen-Or,et al.  4-points congruent sets for robust pairwise surface registration , 2008, ACM Trans. Graph..

[5]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

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

[7]  B. Curless New Methods for Surface Reconstruction from Range Images , 1997 .

[8]  Paul J. Besl,et al.  Active, optical range imaging sensors , 1988, Machine Vision and Applications.

[9]  Kari Pulli,et al.  Multiview registration for large data sets , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[10]  Miao Liao,et al.  Joint depth and alpha matte optimization via fusion of stereo and time-of-flight sensor , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Chitra Dorai,et al.  Shape Spectrum Based View Grouping and Matching of 3D Free-Form Objects , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Ping-Sing Tsai,et al.  Shape from Shading: A Survey , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[14]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Jorge L. C. Sanz,et al.  Advances in Machine Vision , 1988, Springer Series in Perception Engineering.

[16]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.