isoext 0.3.0

A collection of algorithms for iso-sufrace extraction on GPU. Supports pytorch.

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) # must be a cuda pytorchtensor

isolevel = 0

v, f = isoext.marching_cubes(sdf_v, aabb=aabb, level=isolevel, method="lorensen")

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

Marching Cubes

Arguments

isoext.marching_cubes accepts the following arguments:

• grid: A CUDA PyTorch tensor representing the scalar field. It should be a 3D tensor. If cells is provided, grid must be of shape (2N, 2, 2), where N is the number of cells.
• aabb: (Optional) A list or tuple of 6 floats representing the axis-aligned bounding box [xmin, ymin, zmin, xmax, ymax, zmax]. If provided, cells must not be given.
• cells: (Optional) A CUDA PyTorch tensor of shape (2N, 2, 2, 3) representing the cell positions. If provided, aabb must not be given.
• level: The isovalue at which to extract the isosurface. Default is 0.0.
• method: The marching cubes algorithm to use. Currently, only "lorensen" is supported.

Return Value

The function returns a tuple (vertices, faces):

• vertices: A CUDA PyTorch tensor of shape (V, 3) representing the vertex positions.
• faces: A CUDA PyTorch tensor of shape (F, 3) representing the triangular faces.
• If no faces are found, both vertices and faces will be None.

Task List

• Fix docstring.
• Add more Marching Cubes variants.
• Add Dual Contouring.
• Add Dual Marching Cubes.

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/).