DOI: 10.5281/zenodo.10679358
title: ULB Voronoi - Simulated Plenoptic 2.0

description: The sequence "ULB Voronoi - Simulated Plenoptic 2.0" is provided by Daniele Bonatto, Sarah Fachada, Gauthier Lafruit, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.

 

Licence:

CC BY-NC-SA

 

 Terms of Use

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

 

 

[1] Daniele Bonatto, Sarah Fachada, Gauthier Lafruit, "ULB Voronoi - Simulated Plenoptic 2.0", 2024.

 

@misc{bonatto_voronoi_2024,

    title = {{ULB} {Voronoi} - {Simulated} {Plenoptic} {2.0}},

    author = {Bonatto, Daniele and Fachada, Sarah and Lafruit, Gauthier},

    month = feb,

    year = {2024},

    doi = {10.5281/zenodo.10679358}

}

 

[2] D. Bonatto, « From multi-modal capture to photo-realistic view synthesis - A high-quality and real-time multiview approach », Université Libre de Bruxelles, 2024.

 

@thesis{bonatto_multi-modal_2024,

    location = {Brussels, Belgium},

    title = {From multi-modal capture to photo-realistic view synthesis},

    institution = {Universite Libre de Bruxelles},

    type = {phdthesis},

    author = {Bonatto, Daniele},

    date = {2024-03},

}

 

Production

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

 

Content

This dataset contains a static scene (Voronoi) created using the light field blender plugin [4]. We provide calibration images of a squared checkerboard and white images that could be obtained with different main lens apertures. We provide the blender files for the Voronoi dataset.

 

The dataset we generated follows the principle that each micro-lens functions like a pinhole camera. To achieve this, we utilized Blender's light-field plugin [4] to generate an array of cameras that captured a basic scene, thereby simulating the plenoptic camera acquisition process.

 

The Voronoi scene consists of two planes that possess a Voronoi texture and are positioned at distances of $|t_1|=2$mm and $|t_2|=2.5$mm from the \tip{mla}. In order to acquire maximum information, the plenoptic images must be arranged in a typical hexagonal layout [5]. However, the utilized plug-in only allows for rectangular shapes and necessitates equal distances between cameras both horizontally and vertically. Conversely, in hexagonal grids, each row comprises contiguous images, and the vertical spacing has a factor of $\sqrt3/2$ to the horizontal spacing. To account for this discrepancy, we created a $1\times31$ row of contiguous cameras, spaced $B=0.2$mm apart, and shifted it vertically by the corresponding step during 20 frames. To generate the rows between the micro-lenses in the hexagonal arrangement, we rendered a second light-field of the same scene with a shift of $(0.5,\sqrt3/2)B$, resulting in a total of $20\times31$ views per light-field.

 

 

We established the camera parameters as follows: the micro-lenses were positioned in parallel and possessed a focal length of $f=100$mm, a sensor size of $\text{sensor}=35$mm, a diameter of $D=30$pix, and a baseline of $B=0.2$mm. These specifications correspond to a value of $s=\frac{Bf}{\text{sensor}}=0.5714$mm in a plenoptic camera.

 

We developed a script that uses the two light-fields to produce a hexagonal grid and applies a circular mask to generate an image that mimcs the plenoptic ones. The orientation of the micro-image determines whether the resulting plenoptic camera is in Galilean or Keplerian configuration, corresponding to $t_1=\pm2$mm and $t_2=\pm2.5$mm. The obtained Galilean plenoptic image has dimensions of $1095\times945$.

 

The dataset contains:

 - a voronoi.zip file with the scene and depth map, 

 - a whites.zip file with the whites in png format,

 - a checkerboard.zip file with a rotating checkerboard in png format.

 - config_cam{1,2}.blend Blender files [3] for generating the Voronoi.

 

 

References and links:

 

[3] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.

 

[4] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,

https://github.com/lightfield-analysis/blender-addon

https://github.com/dbonattoj/blender-addon

 

[5] Perwass, Christian, et Lennart Wietzke. « Single Lens 3D-Camera with Extended Depth-of-Field », 829108. Burlingame, California, USA, 2012. https://doi.org/10.1117/12.909882.