Abstract
This paper presents an object reconstruction method based on a binocular stereo vision system. First, the relative position and orientation between the two cameras of the system are obtained by a binocular calibration process. Then feature pixels are extracted from images of the two cameras of the same scene according to the SIFT (Scale Invariant Feature Transform) feature. Feature pixels on the image of one camera are matched with those of the other camera according to differences between their SIFT features. And the RANSAC (Random Sample Consensus) algorithm is used to eliminate incorrect matched pixels. Then a 3D coordinate point is obtained from each pair of matched pixels. Finally, 3D models are constructed from the 3D coordinate points through triangulation and texture mapping. In the above processes, a uniform method of calculating coordinates of 3D points from pixel pairs is introduced, which is fitted for arbitrarily orientated optical axes of the left and the right cameras. Experiment results show that the proposed method can obtain 3D points sampled from real objects and produce 3D models consistent with reality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Luo, G.: Some issues of depth perception and three dimention reconstruction from binocular stereo vision. Central South University, China (2012)
Geng, Y.: Research on stereo matching algorithms. Jilin University, China (2014)
Han, H., Han, X., Fang, F.: A new stereo matching method based on edges and corners. J. Comput. Inf. Syst. 8(14), 6041–6048 (2012)
El-etriby, S., Al-hamadi, A.K., Michaelis, B.: Dense depth map reconstruction by phase difference-based algorithm under influence of perspective distortion. Mach. Graph. Vis. 15(3), 349–361 (2006)
Zhang, G., Hua, W., Qin, X., et al.: Stereoscopic video synthesis from a monocular video. IEEE Trans. Vis. Comput. Graph. 13(4), 686–696 (2007)
Liu, K., Zhou, C., Wei, S., et al.: Optimized stereo matching in binocular 3D measurement system using structured light. Appl. Opt. 53(26), 6083–6090 (2014)
Bleyer, M., Gelautz, M.: Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions. Sig. Process. Image Commun. 22(2), 127–143 (2007)
Felzenszwalb, P., Huttenlocher, D.: Efficient belief propagation for early vision. Int. J. Comput. Vis. 70(1), 41–54 (2006)
Gong, M., Yang, Y.H.: Fast unambiguous stereo matching using reliability-based dynamic programming. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 998–1003 (2005)
Lowe, D.G.: Distinctive image features from scale invariant key points. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Martin, A.F., Robert, C.B.: Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Shrivasthava, P., Vundavilli, P.R., Pratihar, D.K.: An approach for 3D reconstruction of environment using stereo-vision system. In: IEEE Region 10 and the Third International Conference on Industrial and Information Systems, pp. 1–7 (2008)
Hartley, R.I.: Theory and practice of projective rectification. Int. J. Comput. Vis. 35(2), 115–127 (1999)
Al-Zahrani, A., Ipson, S.S., Haigh, J.G.B.: Applications of a direct algorithm for the rectification of uncalibrated images. Inf. Sci. 160(1–4), 53–71 (2004)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
Acknowledgment
This work is supported by the National Natural Science Foundation of China (Grant No. 51205332), the SRF for the Returned Overseas Chinese Scholars, and Fujian Science and Technology Major Project (No. 2015HZ0002-1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, Y., Li, C., Gong, J. (2017). An Object Reconstruction Method Based on Binocular Stereo Vision. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10463. Springer, Cham. https://doi.org/10.1007/978-3-319-65292-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-65292-4_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65291-7
Online ISBN: 978-3-319-65292-4
eBook Packages: Computer ScienceComputer Science (R0)