Abstract
Disparity estimation for multi-layered light fields can robustly be performed with a statistical analysis of sparse light field coding coefficients [7]. The key idea is to explain each epipolar plane image patch with a dictionary composed of atoms with known disparity values. We significantly improve upon their approach in two ways. First, we reduce the number of necessary dictionary atoms, improving descriptive quality of each and reducing time complexity by an order of magnitude. Second, we introduce a way to explicitly handle occlusions, which is the main drawback in the previous work. Experiments demonstrate that we thus achieve substantially better results on both Lambertian as well as multi-layered scenes.
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
Bolles, R., Baker, H., Marimont, D.: Epipolar-plane image analysis: an approach to determining structure from motion. Int. J. Comput. Vis. 1(1), 7–55 (1987)
Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40(1), 120–145 (2011)
Goldluecke, B., Wanner, S.: The variational structure of disparity and regularization of 4D light fields. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2013)
Heber, S., Pock, T.: Shape from light field meets robust PCA. In: Proceedings of the European Conference on Computer Vision (2014)
Heber, S., Ranftl, R., Pock, T.: Variational shape from light field. In: International Conference on Energy Minimization Methods for Computer Vision and Pattern Recognition, pp. 66–79 (2013)
Jeon, H., Park, J., Choe, G., Park, J., Bok, Y., Tai, Y., Kweon, I.: Accurate depth map estimation from a lenslet light field camera. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2015)
Johannsen, O., Sulc, A., Goldluecke, B.: What sparse light field coding reveals about scene structure. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2016)
Hosseini Kamal, M., Favaro, P., Vandergheynst, P.: A convex solution to disparity estimation from light fields via the primal-dual method. In: Tai, X.-C., Bae, E., Chan, T.F., Lysaker, M. (eds.) EMMCVPR 2015. LNCS, vol. 8932, pp. 350–363. Springer, Heidelberg (2015)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2010)
Olshausen, B., Field, D.: Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis. Res. 37(23), 3311–3325 (1997)
Tao, M.W., Wang, T.-C., Malik, J., Ramamoorthi, R.: Depth estimation for glossy surfaces with light-field cameras. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8926, pp. 533–547. Springer, Heidelberg (2015)
Tosic, I., Berkner, K.: Light field scale-depth space transform for dense depth estimation. In: Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 441–448 (2014)
Wang, T., Efros, A., Ramamoorthi, R.: Occlusion-aware depth estimation using light-field cameras. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3487–3495 (2015)
Wanner, S., Goldluecke, B.: Globally consistent depth labeling of 4D light fields. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 41–48 (2012)
Wanner, S., Goldluecke, B.: Reconstructing reflective and transparent surfaces from epipolar plane images. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 1–10. Springer, Heidelberg (2013)
Wanner, S., Goldluecke, B.: Variational light field analysis for disparity estimation and super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 606–619 (2014)
Wanner, S., Meister, S., Goldluecke, B.: Datasets and benchmarks for densely sampled 4D light fields. In: Vision, Modelling and Visualization (VMV) (2013)
Acknowledgements
This work was supported by the ERC Starting Grant “Light Field Imaging and Analysis” (LIA 336978, FP7-2014).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Johannsen, O., Sulc, A., Goldluecke, B. (2016). Occlusion-Aware Depth Estimation Using Sparse Light Field Coding. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-45886-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45885-4
Online ISBN: 978-3-319-45886-1
eBook Packages: Computer ScienceComputer Science (R0)