isoext 0.5.1

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

isoext: Isosurface Extraction on GPU

Overview

isoext is a high-performance Python library for GPU-accelerated isosurface extraction.

✨ Key Features

🔷 Different Isosurface Extraction Methods

• Marching Cubes
  • lorensen: the original marching cubes algorithm from the paper Marching cubes: A high resolution 3D surface construction algorithm.
  • nagae: the marching cubes algorithm from the paper Surface construction and contour generation from volume data. It uses only rotation to transform the Marching Cubes cases, unlike lorensen which uses rotation and reflection. lorensen contains ambiguities which results in holes and cracks. This modification removes the ambiguities and produces a closed surface.
• Dual Contouring
  • dual_contouring: the dual contouring algorithm from the paper Dual Contouring of Hermite Data.
• More methods will be added in the future.

🔷 Flexible Grid Support

• Uniform grid for regular sampling
• Sparse grid for memory efficiency
• Octree grid is coming soon

🔷 Developer Tools

• Built-in Marching Cubes table generator
  • All lookup tables used in the library are generated using this tool
• Rich set of SDF primitives and operators
  • Create custom SDFs by combining primitives and operators.

Installation

isoext currently requires PyTorch with CUDA support for GPU acceleration.

Prerequisites

• PyTorch with CUDA support
• CUDA Toolkit matching your PyTorch version
• A compatible C++ compiler (e.g., Visual Studio on Windows, GCC on Linux)

Install from PyPI

The simplest way to install isoext is via pip:

pip install isoext

On Windows, you might encounter compilation errors due to the system’s default limit on maximum path length (260 characters). You can enable long paths on Windows by following tutorials such as this one

Install from Source

git clone https://github.com/GuangyanCai/isoext
cd isoext
pip install .

Quick Start

Here's a simple example to get you started:

import isoext
from isoext.sdf import *

# Create grid
grid = isoext.UniformGrid([256, 256, 256])

# Create composite SDF shape - a sphere with three orthogonal toroidal holes
torus_a = TorusSDF(R=0.75, r=0.15)  # Base torus in xy plane
torus_b = RotationOp(sdf=torus_a, axis=[1, 0, 0], angle=90)  # Rotated to xz plane
torus_c = RotationOp(sdf=torus_a, axis=[0, 1, 0], angle=90)  # Rotated to yz plane
sphere_a = SphereSDF(radius=0.75)
sdf = IntersectionOp([sphere_a, NegationOp(UnionOp([torus_a, torus_b, torus_c]))])

# Evaluate SDF and extract isosurface
sdf_v = sdf(grid.get_points())
grid.set_values(sdf_v)

# Run marching cubes
print("Running marching cubes")
v, f = isoext.marching_cubes(grid)
print("Writing obj")
isoext.write_obj("mc.obj", v, f)
print("Done")

# Run dual contouring
print("Running dual contouring")
its = isoext.get_intersection(grid)
points = its.get_points()
normals = get_sdf_normal(sdf, points)
its.set_normals(normals)
v, f = isoext.dual_contouring(grid, its)
print("Writing obj")
isoext.write_obj("dc.obj", v, f)
print("Done")

Documentation

The documentation is available on the wiki.

Future Plans

• Add Dual Marching Cubes.
• Other more recent isosurface extraction methods.
• Support more libraries such as numpy and jax.

License

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

Acknowledgments

We use the following libraries:

• Thrust for GPU computing.
• cuBLAS and cuSOLVER for QEF solving.
• nanobind for Python binding.