OxVoxNNS - Oxidised Voxelised Nearest Neighbour Search

PyPI Actions Status

A hybrid-ish nearest neighbour search implemented in rust, tailored towards consistent performance, especially on difficult inputs for KDTrees

Installation

Precompiled (from PyPI, recommended)

pip install ox_vox_nns

Manual

Checkout this repo and enter a virtual environment, then run

maturin develop --release

Usage

Basic usage, query a block of query points in sparse mode:

import numpy as np
from ox_vox_nns.ox_vox_nns import OxVoxNNS

NUM_POINTS = 100_000
TEST_POINTS = np.random.random((NUM_POINTS, 3))

indices, distances = ox_vox_nns.OxVoxNNS(
    search_points=TEST_POINTS,
    max_dist=0.05,
).find_neighbours(
    query_points=TEST_POINTS,
    num_neighbours=1000,
    sparse=True,
)

More complex usage, using a single NNS object for multiple exact mode queries (e.g. to distribute the nns object and perform queries in parallel, or to query from a large number of query points in batches/chunks)

# same imports and test data as above

nns = ox_vox_nns.OxVoxNNS(TEST_POINTS, 0.1)

for query_points_chunk in query_points_chunks:
    chunk_indices, chunk_distances = nns.find_neighbours(
        query_points=query_points_chunk,
        num_neighbours=1,
        sparse=False,
    )

Performance

As a rough heuristic:

  • Open3D will edge out OxVox on easier data, e.g. uniformally distributed points, though OxVox does outperform SciPy and SKLearn's KDTree implementations
  • OxVox in exact mode will outperform even Open3D on harder inputs, e.g. with clusters of very dense points
  • OxVox in sparse mode or with epsilon > 0.0 will dramatically outperform KDTrees too. This is not really a fair comparison, but if you don't strictly need the exact k nearest neighbours it can be very helpful

See performance_test_ox_vox_nns.py for test code.

More thorough tests and interactive visualisations are still being developed to help a prospective user decide quickly if OxVox is worth trying

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ham430 from gravatar.com ham430

Credits

Author: Hamish Morgan <ham430@gmail.com>

Requires

Python >=3.8

Tags

nearest neighbour pointcloud

Classifiers

Report project as malware

Metadata

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ham430 from gravatar.com ham430

Credits

Author: Hamish Morgan <ham430@gmail.com>

Requires

Python >=3.8

Tags

nearest neighbour pointcloud

Classifiers

Report project as malware

Release files for ox-vox-nns 0.6.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for ox-vox-nns 0.6.1
File Size Uploaded
ox_vox_nns-0.6.1.tar.gz 20.0 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for ox-vox-nns 0.6.1
File
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ox_vox_nns-0.6.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl 553.1 kB CPython 3.8 CPython 3.8 Linux glibc 2.17+ IBM System/390x Details
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ox_vox_nns-0.6.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl 512.5 kB CPython 3.8 CPython 3.8 Linux glibc 2.17+ x86-32 Details
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Total release size: 50.2 MB

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Tags CPython 3.8 Linux glibc 2.17+ ARM64
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