||Lytro (1st generation) dataset: The full dataset is described and available here for download. When using this dataset in your research, please cite A. Mousnier, E. Vural, C. Guillemot, Partial light field tomographic reconstruction from a fixed-camera focal stack, https://arxiv.org/abs/1503.01903. March 2015. (pdf)|
|Lytro (Illum) dataset: The full dataset is described and available for download here When using this dataset in your research, please cite M. Le Pendu, X. jiang, C. Guillemot, Light Field inpainting propagation via low rank matrix completion, IEEE Trans. on Image Processing, vol. 27, No. 4, pp. 1981-1993, Jan. 2018.|
Lytro 1st generation and Illum dataset: A larger dataset captured by both a first generation Lytro and an Illum camera can be retrieved here.
When using this dataset in your research, we will be happy if you cite X. jiang, M. Le Pendu, R. Farrugia, C. Guillemot, Light Field Compression with Homography-based Low Rank Approximation, special issue on Light Field Image Processing of the IEEE J. on Selected Topics in Signal Processing, IEEE J-STSP, vol. 11, No. 7, pp. 1132-1145, Oct. 2017.
|Raytrix (R8) dataset: Ligh field video dataset captured by a R8 Raytrix camera (with disparity maps). When using this dataset in your research, we will appreciate if you cite L. Guillo, X. jiang, G. Lafruit, C. Guillemot, Light field video dataset captured by a R8 Raytrix camera (with disparity maps), ISO /IEC JTC1/SC29/WG11 MPEG2018/m42468, ISO/IEC JTC1/SC29/WG1 JPEG2018/m79046, INTERNATIONAL ORGANISATION FOR STANDARDISATION,ISO/IEC JTC1/SC29/WG1 & WG11, April 2018, San Diego, CA, US.(pdf)|
|Synthetic video light fields based on the Sintel movie modified in the Blender 3D software in order to render an array of 3x3 views. We have modified the scenes to generate not only the final render, but also a clean render without lighting effects, motion blur, or semi-transparent objects. This dataset is composed of two synthetic light fields (Bamboo2 and Temple1) of 3 x 3 views of 1024 x 536 pixels and 50 frames. The light field views are provided with the corresponding ground truth scene flow (optical flow and disparity variation). When using this dataset in your research, we will appreciate if you cite P. David, M. Le Pendu, C. Guillemot, Sparse to dense scene flow estimation from light fields, submitted, ICIP 2019.(pdf)|
|Inria synthetic light field datasets are synthetic light field datasets rendered with Blender 3D software. They contain a densely sampled light field dataset and a sparsely sampled one. The light fields in both datasets are of spatial resolution 512 x 512 and angular resolution 9 x 9. We offered a sub-aperture image (png format) and a disparity map (npy/mat format) for each viewpoint in light field. When using our datasets, we will appreciate it if you can cite our article: J. Shi, X. Jiang, C. Guillemot, A framework for learning depth from a flexible subset of dense and sparse light field views. TIP, July 2019.(pdf)|
The raw Lytro light fields data can be decoded with the matlab light field toolbox. The decoding process includes de-vignetting, color de-mosaicking, conversion of the hexagonal to a rectangular sampling grid, colour correction, and extraction of the sub-aperture images. Matlab functions implementing improved demosaicing and alignment methods can be downloaded below.
All datasets and benchmarks on this page are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.
This version is called v0.4-CLIM as it originates from D.Dansereau's LF toolbox v0.4. The following features have been added: