A fast nearest neighbor index cuda implementation for 3-D points using a left balanced BVH-tree.
Project description
Cupy-KNN
This package provides a fast nearest neighbor index for 3-D points using a Cupy implementation of a left balanced BVH-tree.
Installation
Using pip
pip install cupy-knn
From source
git clone https://github.com/mortacious/cupy-knn.git
cd cupy-knn
python setup.py install
Acknowledgements
This package is inspired by the approach presented in the following paper:
@inproceedings{jakob2021optimizing,
title={Optimizing LBVH-Construction and Hierarchy-Traversal to accelerate kNN Queries on Point Clouds using the GPU},
author={Jakob, Johannes and Guthe, Michael},
booktitle={Computer Graphics Forum},
volume={40},
number={1},
pages={124--137},
year={2021},
organization={Wiley Online Library}
}
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
cupy-knn-0.2.5.tar.gz
(24.2 kB
view details)
Built Distribution
cupy_knn-0.2.5-py3-none-any.whl
(26.5 kB
view details)
File details
Details for the file cupy-knn-0.2.5.tar.gz
.
File metadata
- Download URL: cupy-knn-0.2.5.tar.gz
- Upload date:
- Size: 24.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.15
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | f9cdae46f168e17d4181aaf37376a51c45a5dc74718446a3594e1c855fef95ca |
|
MD5 | c97f9c6048516d6b26b3ec4faa3f36cc |
|
BLAKE2b-256 | e4b11f19a7674e7e703e91e0415393fa4b494a05d0625ae37fca6cfb8236fef7 |
File details
Details for the file cupy_knn-0.2.5-py3-none-any.whl
.
File metadata
- Download URL: cupy_knn-0.2.5-py3-none-any.whl
- Upload date:
- Size: 26.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.15
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 179b513755c4ff283aff49f8cb062a82668b50022705de5cb0cf09d26807e8ba |
|
MD5 | 94b1fc426e905f0a6eebafe784089016 |
|
BLAKE2b-256 | 4c88ad4eea6df2c991e83ba8939c3bbf707497381fbfc07cc0f6bd9f6fc64e37 |