3D reconstruction system for semi-automatic estimation of objects length and area by means of stereo vision

Bryan García, Carlos Diego Ferrin B., Jorge Humberto Erazo


It is mandatory to characterize dimensionally the manufactured industrial pieces for quality control purposes. As it is not possible to touch some pieces when trying to retrieve dimensional information, then non-invasive techniques are required to do so. Stereo vision is a passive technology which is both robust and accurate for non-invasive applications. For this reason, in this work we describe the design and implementation of a 3D reconstruction system for the estimation of the length and area of certain objects. This tool allows to easily incorporate new image correspondence techniques to its main execution pipeline. We carry some experiments and show certain benefits when selecting an accurate image correspondence technique for the estimation of the length and area.


Stereo vision; 3D reconstruction; digital image processing.

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Bodkin, B. H. (2012). Real-Time Mobile Stereo Vision. University of Tennessee. Retrieved from http://trace.tennessee.edu/utk_gradthes/1313

Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., & Ranzuglia, G. (2008). MeshLab: an Open-Source Mesh Processing Tool. Sixth Eurographics Italian Chapter Conference, (pp. 129-136). doi:10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2008/129-136

De Loera, J. A., Rambau, J., & Santos, F. (2010). Triangulations (Vol. 25). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-12971-1

Demant, C., Streicher-Abel, B., & Garnica, C. (2013). Industrial image processing. Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-33905-9

Ferrin, C. (2017). UNIAJC: Proyecto visión estéreo. Retrieved from https://github.com/cdfbdex/UNIAJC_VISION-ESTEREO

Foix, S. & Aleny, G. (2011). Lock-in time-of-flight (ToF) cameras: A survey. IEEE Sensors Journal, 11(3). doi: 10.1109/JSEN.2010.2101060

Hagen, H., Disch, A., Ehret, J., Klein, R., Sascha, K., Zeckzer, D., & Michael, M. (n.d.). Visual inspection methods for quality control in automotive engineering. Visualization, 2004 IEEE. doi:10.1109/VISUAL.2004.111

Hamzah, R. A., Hamid, A. M. A., & Salim, S. I. M. (2010). The solution of stereo correspondence problem using block matching algorithm in stereo vision mobile robot. 2010 Second International Conference on Computer Research and Development, (pp. 733-737). doi:10.1109/ICCRD.2010.167

Hamzah, R. A. & Ibrahim, H. (2016). Literature survey on stereo vision disparity map algorithms. Journal of Sensors, 2016. Art. 8742920. doi:10.1155/2016/8742920

Hansard, M., Lee, S., Choi, O., & Horaud, R. (2013). Time of flight cameras: Principles, methods, and applications. London, UK: Springer. https://doi.org/10.1007/978-1-4471-4658-2

Kim, M. Y., Ayaz, S. M., Park, J., & Roh, Y. (2014). Adaptive 3D sensing system based on variable magnification using stereo vision and structured light. Optics and Lasers in Engineering, 55, 113-127. doi:10.1016/j.optlaseng.2013.10.021

Krig, S. (2014). Computer Vision Metrics. Computer Vision Metrics: Survey, Taxonomy, and Analysis. Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4302-5930-5

Lee, S., Lee, J. H., Lim, J., & Suh, I. H. (2015). Robust stereo matching using adaptive random walk with restart algorithm. Image and Vision Computing, 37, 1-11. doi:10.1016/j.imavis.2015.01.003

Szeliski, R. (2011). Computer vision. Media (Vol. 42). London, UK: Springer. doi:10.1007/978-1-84882-935-0

Wöhler, C. (2013). 3D Computer vision. London, UK: Springer. doi:10.1007/978-1-4471-4150-1

Zhang, S., Wang, C., & Chan, S. C. (2013). A new high resolution depth map estimation system using stereo vision and kinect depth sensing. Journal of Signal Processing Systems, 79(1), 19-31. doi:10.1007/s11265-013-0821-8

DOI: http://dx.doi.org/10.18046/syt.v15i40.2372


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