Source code repository for the paper "Radial Intersection Count Image: a Clutter Resistant Shape Descriptor"
Please find the paper on which this work is based here
This repository contains:
- A reference implementation of the Radial Intersection Count Image
- Efficient GPU implementations of the Spin Image and 3D Shape Context descriptors
- A reference implementation of the Clutterbox Experiment
- A script which can be used to completely reproduce all results presented in the paper
You can run the start the script by running:
python3 replicate.py
From the root of the repository.
Refer to the included Manual PDF for further instructions.
The RAM and Disk space requirements are only valid when attempting to reproduce the presented results.
The codebase should be able to compile on Windows, but due to some CUDA driver/SDK compatbility issues we have not yet been able to verify this.
| Type | Requirements |
|---|---|
| CPU | Does not matter |
| RAM | At least 32GB |
| Disk | Must have about ~70GB of storage available to store the downloaded datasets |
| GPU | Any NVIDIA GPU (project uses CUDA) |
| OS | Ubuntu 16 or higher. Project has been tested on 18 and 20. |
- Development and implementation: Bart Iver van Blokland, NTNU Visual Computing Lab
- Supervision: Theoharis Theoharis, NTNU Visual Computing Lab
If you use (parts of) this library in your research, we kindly ask you reference the papers on which this project is based:
@article{van2020radial,
title={Radial intersection count image: A clutter resistant 3D shape descriptor},
author={van Blokland, Bart Iver and Theoharis, Theoharis},
journal={Computers \& Graphics},
volume="91",
pages="118--128",
year={2020},
publisher={Elsevier}
}
@article{van2020indexing,
title={An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying},
author={van Blokland, Bart Iver and Theoharis, Theoharis},
journal={Computers \& Graphics},
volume="92",
pages="55--66",
year={2020},
publisher={Elsevier}
}