Abstract
This paper is concerned with the problem of multi-view 3D reconstruction with an un-calibrated micro-lens array based light field camera. To acquire 3D Euclidean reconstruction, existing approaches commonly apply the calibration with a checkerboard and motion estimation from static scenes in two steps. Self-calibration is the process of simultaneously estimating intrinsic and extrinsic parameters directly from un-calibrated light fields without the help of a checkerboard. While the self-calibration technique for conventional (pinhole) camera is well understood, how to extend it to light field camera remains a challenging task. This is primarily due to the ultra-small baseline of the light field camera. We propose an effective self-calibration method for a light field camera for automatic metric reconstruction without a laborious pre-calibration process. In contrast to conventional self-calibration, we show how such a self-calibration method can be made numerically stable, by exploiting the regularity and measurement redundancies unique for the light field camera. The proposed method is built upon the derivation of a novel ray-space homography constraint (RSHC) using Plücker parameterization as well as a ray-space infinity homography (RSIH). We also propose a new concept of “rays of the absolute conic (RAC)” defined as a special quadric in 5D projective space \({\mathbb {P}}^5\). A set of new equations are established and solved for self-calibration and 3D metric reconstruction specifically designed for a light field camera . We validate the efficacy of the proposed method on both synthetic and real light fields, and have obtained superior results in both accuracy and robustness.










Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Bartoli, A., & Sturm, P. (2001). The 3d line motion matrix and alignment of line reconstructions. In IEEE conference on computer vision and pattern recognition (CVPR) (Vol. I, pp. I–I). IEEE.
Bartoli, A., & Sturm, P. (2004). The 3d line motion matrix and alignment of line reconstructions. International Journal of Computer Vision, 57, 159–178.
Bartoli, A., & Sturm, P. (2005). Structure-from-motion using lines: Representation, triangulation, and bundle adjustment. Computer Vision and Image Understanding, 100(3), 416–441.
Birklbauer, C., & Bimber, O. (2014). Panorama light-field imaging. Computer Graphics Forum, 33(2), 43–52.
Bok, Y., Jeon, H. G., & Kweon, I. S. (2014). Geometric calibration of micro-lens-based light-field cameras using line features. In European conference on computer vision (ECCV) (pp 47–61).
Bok, Y., Jeon, H. G., & Kweon, I. S. (2017). Geometric calibration of micro-lens-based light field cameras using line features. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 39(2), 287–300
Chandraker, M., Agarwal, S., Kahl, F., Nistér, D., & Kriegman, D. (2007a). Autocalibration via rank-constrained estimation of the absolute quadric. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1–8).
Chandraker, M., Agarwal, S., Kriegman, D., & Belongie, S. (2007b). Globally optimal affine and metric upgrades in stratified autocalibration. In IEEE international conference on computer vision (ICCV) (pp. 1–8).
Dansereau, D. G., Mahon, I., Pizarro, O., & Williams, S. B. (2011). Plenoptic flow: Closed-form visual odometry for light field cameras. In 2011 IEEE/RSJ international conference on intelligent robots and systems (pp. 4455–4462).
Dansereau, D. G., Pizarro, O., & Williams, S. B. (2013). Decoding, calibration and rectification for lenselet-based plenoptic cameras. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1027–1034).
Dong, F., Ieng, S. H., Savatier, X., Etienne-Cummings, R., & Benosman, R. (2013). Plenoptic cameras in real-time robotics. The International Journal of Robotics Research, 32(2), 206–217.
Faugeras, O. (1993). Three-dimensional computer vision: a geometric viewpoint. MIT Press.
Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395.
Gherardi, R., & Fusiello, A. (2010). Practical autocalibration. In European conference on computer vision (ECCV) (pp. 790–801).
Guo, X., Yu, Z., Kang, S. B., Lin, H., & Yu, J. (2016). Enhancing light fields through ray-space stitching. IEEE Transactions on Visualization and Computer Graphics, 22(7), 1852–1861.
Gurdjos, P., Bartoli, A., Sturm, P. (2009). Is dual linear self-calibration artificially ambiguous? In International conference on computer vision (ICCV) (pp. 88–95). IEEE.
Habed, A., Pani Paudel, D., Demonceaux, C., & Fofi, D. (2014). Efficient pruning LMI conditions for branch-and-prune rank and chirality-constrained estimation of the dual absolute quadric. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 493–500).
Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge University Press.
Hartley, R., Trumpf, J., Dai, Y., & Li, H. (2013). Rotation averaging. International Journal of Computer Vision, 103(3), 267–305.
Hartley, R. I., & Sturm, P. (1997). Triangulation. Computer vision and image understanding, 68(2), 146–157.
Hartley, R. I., Hayman, E., de Agapito, L., & Reid, I. (1999). Camera calibration and the search for infinity. In IEEE international conference on computer vision (ICCV) (pp. 510–517).
Johannsen, O., Sulc, A., & Goldluecke, B. (2015). On linear structure from motion for light field cameras. In IEEE international conference on computer vision (ICCV) (pp. 720–728).
Johannsen, O., Sulc, A., Marniok, N., & Goldluecke, B. (2016). Layered scene reconstruction from multiple light field camera views. In Asian conference on computer vision (ACCV) (pp. 3–18)
Kneip, L., & Li, H. (2014). Efficient computation of relative pose for multi-camera systems. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 446–453).
Larsson, V., Kukelova, Z., & Zheng, Y. (2018). Camera pose estimation with unknown principal point. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2984–2992).
Levoy, M., & Hanrahan, P. (1996). Light field rendering. In Proceedings of the 23rd annual conference on computer graphics and interactive techniques (pp. 31–42).
Li, H., Hartley, R., & Kim, J. H. (2008). A linear approach to motion estimation using generalized camera models. In IEEE conference on computer vision and pattern recognition (CVPR) (pp 1–8).
Li, Y., Zhang, Q., Wang, X., & Wang, Q. (2019). Light field slam based on ray-space projection model. In Optoelectronic imaging and multimedia technology VI (Vol. 11187, p 1118706).
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
Luong, Q. T., & Faugeras, O. D. (1997). Self-calibration of a moving camera from point correspondences and fundamental matrices. International Journal of Computer Vision, 22(3), 261–289.
Lytro. (2011). Lytro redefines photography with light field cameras. http://www.lytro.com.
Maybank, S. J., & Faugeras, O. D. (1992). A theory of self-calibration of a moving camera. International Journal of Computer Vision, 8(2), 123–151.
Ng, R. (2006). Digital light field photography. PhD thesis, Stanford University.
Ng, R., Levoy, M., Brédif, M., Duval, G., Horowitz, M., Hanrahan, P., et al. (2005). Light field photography with a hand-held plenoptic camera. Computer Science Technical Report, 2(11), 1–11.
Nistér, D. (2004). Untwisting a projective reconstruction. International Journal of Computer Vision, 60(2), 165–183.
Nousias, S., Lourakis, M., & Bergeles, C. (2019). Large-scale, metric structure from motion for unordered light fields. In IEEE conference on computer vision and pattern recognition (CVPR) (pp 3292–3301).
Paudel, D. P., Van Gool, L. (2018). Sampling algebraic varieties for robust camera autocalibration. In European conference on computer vision (ECCV) (pp. 275–292)
Pless, R. (2003). Using many cameras as one. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2, 587–593.
Pollefeys, M., & Van Gool, L. (1999). Stratified self-calibration with the modulus constraint. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 21(8), 707–724
Pollefeys, M., Koch, R., & Van Gool, L. (1999). Self-calibration and metric reconstruction inspite of varying and unknown intrinsic camera parameters. International Journal of Computer Vision, 32(1), 7–25.
Pottmann, H., & Wallner, J. (2009). Computational line geometry. Springer Science & Business Media.
Raytrix. (2013). 3d light field camera technology. http://www.raytrix.de.
Ren, Z., Zhang, Q., Zhu, H., & Wang, Q. (2017). Extending the FOV from disparity and color consistencies in multiview light fields. In IEEE international conference on image processing (ICIP) (pp. 1157–1161).
Seo, Y., Heyden, A., & Cipolla, R. (2001). A linear iterative method for auto-calibration using the dac equation. In IEEE conference on computer vision and pattern recognition (CVPR).
Sturm, P. (2005). Multi-view geometry for general camera models. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1, 206–212.
Triggs, B. (1997). Autocalibration and the absolute quadric. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 609—-614).
Vianello, A., Ackermann, J., Diebold, M., & Jähne, B. (2018). Robust hough transform based 3d reconstruction from circular light fields. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 7327–7335).
Zhang, Q., & Wang, Q. (2018). Common self-polar triangle of concentric conics for light field camera calibration. In Asian conference on computer vision (ACCV) (pp. 18–33).
Zhang, Q., Ling, J., Wang, Q., & Yu, J. (2019a). Ray-space projection model for light field camera. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 10121–10129).
Zhang, Q., Wang, X., & Wang, Q. (2019b). Light field planar homography and its application. In Optoelectronic imaging and multimedia technology VI (Vol. 11187, p. 111870S).
Zhang, Q., Zhang, C., Ling, J., Wang, Q., & Yu, J. (2019c). A generic multi-projection-center model and calibration method for light field cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 41(11), 2539–2552.
Zhang, Q., Wang, Q., Li, H., & Yu, J. (2020). Ray-space epipolar geometry for light field cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 1. https://doi.org/10.1109/TPAMI.2020.3025949.
Zhang, Y., Li, Z., Yang, W., Yu, P., Lin, H., & Yu, J. (2017a). The light field 3d scanner. In IEEE international conference on computational photography (ICCP) (pp. 1–9).
Zhang, Y., Yu, P., Yang, W., Ma, Y., & Yu, J. (2017b). Ray space features for plenoptic structure-from-motion. In IEEE international Conference on computer vision (ICCV) (pp. 4631–4639).
Acknowledgements
The work was supported by NSFC under Grant 61531014, 61801396, 62031023. We thank the editors and reviewers for valuable suggestions on contents and experiments. We also thank Ying Feng for helpful supports on data collection. Qi Zhang was also supported by Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under CX201919 and China Scholarship Council (CSC).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Communicated by Adrien Bartoli.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The work was supported by NSFC under Grant 61531014, 61801396, 62031023.
Rights and permissions
About this article
Cite this article
Zhang, Q., Li, H., Wang, X. et al. 3D Scene Reconstruction with an Un-calibrated Light Field Camera. Int J Comput Vis 129, 3006–3026 (2021). https://doi.org/10.1007/s11263-021-01516-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11263-021-01516-1