isoext: Isosurface Extraction on GPU

Overview

Welcome to isoext — a Python library designed for efficient isosurface extraction, leveraging the power of GPU computing and comes with pytorch support. Our library attempts to implement a collection of classic isosurface extraction algorithms. Currently, only the following algorithms are supported, but more will come in the future:

• Marching cubes
  • lorensen: the original marching cubes algorithm from the paper Marching cubes: A high resolution 3D surface construction algorithm.

Installation

To install isoext, make sure CUDA Toolkit is installed and run:

pip install isoext

Quick Start

Here's a simple example to get you started:

import isoext
from isoext.sdf import *

aabb = [-1, -1, -1, 1, 1, 1]
res = 128
grid = isoext.make_grid(aabb, res)

torus_a = TorusSDF(R=0.75, r=0.15)
torus_b = RotationOp(sdf=torus_a, axis=[1, 0, 0], angle=90)
torus_c = RotationOp(sdf=torus_a, axis=[0, 1, 0], angle=90)

sphere_a = SphereSDF(radius=0.75)

sdf = IntersectionOp([
    sphere_a, 
    NegationOp(UnionOp([
        torus_a, torus_b, torus_c
    ]))
])
sdf_v = sdf(grid) # Only accept a gpu tensor from pytorch for now

isolevel = 0

v, f = isoext.marching_cubes(sdf_v, aabb, isolevel)

isoext.write_obj('test.obj', v, f)

Task List

• Fix docstring.
• Implement MC33.
• Add numpy support.

License

isoext is released under the MIT License. Feel free to use it in your projects.

Acknowledgments

• We use Thrust for GPU computing and nanobind for Python binding.
• The LUTs for lorensen are borrowed from Paul Bourke (https://paulbourke.net/geometry/polygonise/).