Since some of the source files are too big, the source can be found here: https://gitlab.lms.tf.fau.de/frank.sippel/hyvid
The actual data can be found here: https://drive.google.com/drive/folders/1JWPaA_w0LW-v4JmYigPaMVVSA0pi6IDZ?usp=sharing
Our novel synthetic Hyperspectral Video Database (HyViD) provides seven scenes rendered from 400 nm to 700 nm in 10 nm steps, resulting in 31 hyperspectral channels. The videos have a length of 30 frames. Furthermore, the scenes are rendered using a camera array using nine cameras arranged in a three times three grid. Depth maps for every camera and frame are provided as well. The camera array is arranged as follows:
[View from front onto the sensor]
0 - 1 - 2
| | |
3 - 4 - 5
| | |
6 - 7 - 8
Thus, camera 4 is the center camera.
The folder structure is:
camera -> channel -> frame
If you execute main.py using Python3 everything should work as long as Blender is installed and executable in command line by blender. You can also execute "run.py" within Blender if you want to model a new scene and execute it. If you add textures to objects, the name of an "Image Texture" must be the same as the name of the folder. If you want to move the camera, please move the "cam_center". This camera is used to calculate the positions of the other cameras. Currently, if you name a light source "sun", then the spectrum of the sun is applied. Otherwise, if the name of a light source ends with 'K', then the number in front of it determines the light spectrum according to the black body radiation, e.g., a light source with name 3200K will lead to a light spectrum of a black body at 3200K temperature.
The database and source are licensed using CC-BY-SA. If you use the dataset or source for your research, you should cite the follwing paper:
@article{Sippel:23,
author = {Frank Sippel and J\"{u}rgen Seiler and Andr\'{e} Kaup},
journal = {J. Opt. Soc. Am. A},
number = {3},
pages = {479--491},
publisher = {Optica Publishing Group},
title = {Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video Coding},
volume = {40},
month = {Mar},
year = {2023},
}
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Medieval Seaport: Scene: https://blendswap.com/blend/6115
City: Scene: https://www.blendswap.com/blend/25505 Car: https://www.blendswap.com/blend/16710
Outdoor: Scene: https://www.blendswap.com/blend/5957
Indoor: Scene: https://www.blendswap.com/blend/6468
Lab: Small man: https://blendswap.com/blend/14431 Cups: https://blendswap.com/blend/4499 Figure: https://blendswap.com/blend/4499 Cardboard: https://blendswap.com/blend/19402 Porsche: https://blendswap.com/blend/11182 White lily: https://blendswap.com/blend/12953 Monstera: https://blendswap.com/blend/27517 Table: https://www.blendswap.com/blend/17037