Published December 19, 2023
| Version v2
Dataset
Open
UA - Gaussian Depth Disc (GDD dataset)
Authors/Creators
-
1.
University of Antwerp
Description
Dear reader,
Welcome! You must be an avid profilometry person to be interested in downloading our dataset.
Before you start tinkering with the dataset package please do install the requirements.txt libraries for a more easy step into operating this system.
We hope to have made the hierarchy of the package as clear as possible! Also note that this system was written in VScode.
The Matlab script that creates the surface has been included for if you yourself would like to print one out and do in depth research!
Find your way to the examples folder, there you can find "entire_dataset". This folder contains a script to divide the original h5 file containing all data
into whatever sub-options you'd like. An example divided dataset has already been given namely the 80/20 division of respectively training and validation
data in the "example_dataset" folder.
In the folder models you will find the two models mentioned in the publication related to this dataset. These two were published with the dataset since they
had either the highest performance on the training and validation dataset (DenseNet) or on the random physical object test (CNN).
A training script is included (training_script.py) to show you how these models were created, so if you wish to add new models to the networks.py file in the classes folder, you can!
The validation jupyter notebook contains two visualisation tools to quickly and neatly show the performance of your model on the recorded dataset.
Lastly to test on the recorded objects you can run the "test_physical_data.py" and "test_physical_data_sine_deivation.py" scripts.
We hope this helps you in your research and we hope it further improves any and all research within the single shot profilometry field! 😊
Kind regards,
Rhys Evans,
InViLab,
University of Antwerp, Belgium
Files
GDD_dataset_package.zip
Files
(13.8 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:60b2ae3fb182af6061fb5a2b2e81e43c
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13.8 GB | Preview Download |
|
md5:40077ad2a396320f579836a2ee27091e
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1.8 kB | Preview Download |
Additional details
Additional titles
- Other (English)
- Deep Learning for Single-Shot Structured Light Profilometry: A Comprehensive Dataset and Performance Analysis