DOI: 10.5281/zenodo.12721988
title: Plenoptic 2.0 - RayTrix R5 and RayTrix R8 - Cards - Calibration and Multiview

description: The test sequence "Plenoptic 2.0 - RayTrix R5 and RayTrix R8 - Cards - Calibration and Multiview" is provided by Sarah Fachada, Daniele Bonatto, Gauthier Lafruit and Mehrdad Teratani, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium.

License

CC BY-NC-SA

Terms of Use

Any kind of publication or report using this sequence should refer to the following references:

[1] **Sarah Fachada, Daniele Bonatto, Gauthier Lafruit and Mehrdad Teratani, "Plenoptic 2.0 - RayTrix R5 and RayTrix R8 - Cards - Calibration and Multiview" 2024.07, 10.5281/zenodo.12721987**



    @misc{fachada_cards_2024,        title = {{Plenoptic} 2.0 } {Raytrix} {R5} and {Raytrix} {R8} - {Cards} - {Calibration} and {Multiview}},        author = {Fachada, Sarah and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad},        month = jul,        year = {2024},        doi = {10.5281/zenodo.12721987}    }


[2] **Sarah Fachada, Daniele Bonatto, Gauthier Lafruit and Mehrdad Teratani, "Pattern and Subaperture-free Calibration of Plenoptic 2.0 cameras" 2024, European Workshop on Visual Information Processing (EUVIP), Geneva, Switzerland**



   @inproceedings{fachada_pattern_2024,        address = {Geneva, Switzerland},        title = {Pattern and {Subaperture}-free {Calibration} of {Plenoptic} 2.0 cameras},        booktitle = {2024 {European} {Workshop} on {Visual} {Information} {Processing}},        author = {Fachada, Sarah and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad},        month = sep,        year = {2024},}


Production

Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Université Libre de Bruxelles, Belgium, 

Content

This dataset provides multiple views of a scene made of cards placed on two depth planes, as well as calibration images taken with the same camera intrinsic parameters. The dataset was acquired with two Raytrix cameras [1], the R5 and the R8, mounted on the Deutsches Zentrum für Luft und Raumfahrt (DLR)'s robotic arm. 

The dataset additionally contains calibration images of a checkerboard and white images taken with different main lens apertures, suited for Calibration with Compote [2].

We provide disparity maps computed with Plenoptic Toolbox [3].

The cameras have a C-mount main lens of focal length 35mm and a focus distance of 375mm (R5) and 315mm (R8). The f-number used to capture the scene and the checkerboard is 3.4 (R5) and 3.2 (R8).

The scene views 1 to 6 have been captured by cameras parallel to the scene. In they coordinate frame, the scene's nearest plane is placed at 360mm from the main lens mount, and the farthest plane is placed at 415mm.

The dataset contains



A `color` folder containing the processed plenoptic images of the scene in PNG format

A `disparities` folder containing:



Associated disparity maps in PNG format

The minimal and maximal disparities corresponding to the values 0 and 255



A `checkerboard` folder containing the plenoptic raw images in PNG format of a checkerboard with square size of 6mm

A `white` folder containing the plenoptic raw images in PNG format of overexposed images captured with a white diffusor and different main lens apertures

A `Calibrationfiles` folder containing:



Raytrix XML file with the specification of the MLA

Compote JS files with the initial camera parameters (`camera.js`), images path (`images.js`) and checkerboard parameters (`scene.js`).




References and links

[1] https://raytrix.de/[2] M. Labussière, C. Teulière, F. Bernardin, and O. Ait-Aider, "Leveraging blur information for plenoptic camera calibration", International Journal of Computer Vision, nᵒ 2012, p. 1‑23, mai 2022, doi: 10.1007/s11263-022-01582-z.[3] L. Palmieri, R. Koch, et R. O. H. Veld, "The Plenoptic 2.0 Toolbox: Benchmarking of Depth Estimation Methods for MLA-Based Focused Plenoptic Cameras", in 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, 2018, p. 649‑653. doi: 10.1109/ICIP.2018.8451073.

Acknowledgments

Sarah Fachada is a Postdoctoral Researcher of the Fonds de la Recherche Scientifique - FNRS, Belgium. This work was supported in part by the HoviTron project (no. 951989), the FER 2021 project (no. 1060H000066-FAISAN), the Emile DEFAY 2021 project (no. 4R00H000236), and the FER 2023 project (no. 1060H000075). The authors thank the Deutsches Zentrum für Luft und Raumfahrt (DLR) for the dataset acquisition.