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Octree structure containing 3D triangular mesh model

Project Description

Octree structure containing a 3D triangular mesh model. To be used for ray tracing / shadow casting.

Written in C++ for speed, but exposed to Python using Cython.


Pyoctree uses an adaptive structure, so it will automatically divide branches to ensure that there are no more than 200 objects per leaf.

Intersection testing uses parallel processing via OpenMP. To use more than a single processor, set value of environment variable OMP_NUM_THREADS to number of desired processors.


  • Python 2.7 or Python >= 3.5
  • vtk >= v6.2.0 or >= v7.0 (optional, for outputting a vtk file for viewing octree structure in Paraview)
  • Cython >= v0.20 and a C++ compiler for building the extension module. Suggested compilers are:
    • The Microsoft C++ Compiler for Python 2.7 if using Python 2
    • Microsoft Visual C++ 2015 (14.0) if using Python 3
    • gcc on Linux
    • Mingw32 on Windows or Linux

Note that a compiler is not required if installing using the provided Python wheel.


1. Building from source

In a command prompt, browse to the base directory containing the file and type:

python install

2. Installation using Python wheel

Download the python wheel from releases i.e. pyoctree-0.2.2-cp27-cp27m-win_amd64.whl for Python 2.7 on Windows 64-bit. Then, open a command prompt, browse to the download directory and type:

pip install pyoctree-0.2.2-cp27-cp27m-win_amd64.whl


1. Creating the octree representation of a 3D triangular mesh model

from pyoctree import pyoctree as ot
tree = ot.PyOctree(pointCoords,connectivity)


  • pointCoords is a Nx3 numpy array of floats (dtype=float) representing the 3D coordinates of the mesh points
  • connectivity is a Nx3 numpy array of integers (dtype=np.int32) representing the point connectivity of each tri element in the mesh

2. Finding intersection between mesh object and ray

The octree can be used to quickly find intersections between the object and a ray. For example:

import numpy as np
startPoint = [0.0,0.0,0.0]
endPoint   = [0.0,0.0,1.0]
rayList    = np.array([[startPoint,endPoint]],dtype=np.float32)
intersectionFound  = tree.rayIntersection(rayList)


Some Jupyter notebooks are provided in the Examples directory on how to use pyoctree.


If help is required, please create an issue on Github.

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
pyoctree-0.2.2-cp27-cp27m-win_amd64.whl (2.3 MB) Copy SHA256 Checksum SHA256 cp27 Wheel May 27, 2017
pyoctree-0.2.2-cp36-cp36m-win_amd64.whl (2.3 MB) Copy SHA256 Checksum SHA256 cp36 Wheel May 27, 2017
pyoctree-0.2.2.tar.gz (2.2 MB) Copy SHA256 Checksum SHA256 Source May 27, 2017

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