isoext

GPU-accelerated iso-surface extraction for PyTorch

An iso-surface is the set of points where a 3D scalar field equals a chosen value: the shape described by a signed distance function, or the boundary of a density volume. Fields like these come from neural networks, simulations and scans, while most tools consume triangle meshes. isoext is a growing collection of iso-surface extraction methods that turn such fields into triangle meshes on the GPU. The field values come in as a PyTorch tensor and the mesh comes back as tensors, so it fits directly into training loops and other GPU pipelines.

• Extraction methods — sharing one grid interface, with more on the way
  • Marching Cubes — the classic primal method, with a choice of lookup tables
    • vega (default) — MC33 with the corrected interior test
    • lewiner — the topology-correct MC33 tables of scikit-image
    • nagae — reflection-free tables
    • lorensen — the original 1987 tables
  • Marching Tetrahedra — ambiguity-free extraction by splitting cells into tetrahedra
  • Dual Contouring — one vertex per cell, placed on sharp features
    • ju (default) — from surface normals, the original QEF method
    • carrera — from the signed distance samples alone, without normals
  • Surface Nets — smooth dual meshes without needing normals
  • Dual Marching Cubes — sharp features with one vertex per surface sheet, so crossing sheets stay separate; takes any of the marching cubes tables above
• Grids
  • Dense uniform grids for full volumes
  • Sparse grids that only store cells near the surface, so memory scales with area instead of volume
• Interactive viewer — meshes and grid overlays in the browser, built on viser; scenes can be embedded in static web pages
• SDF toolbox — primitives from spheres to a Mandelbulb, CSG operations, signed distances to triangle meshes on a GPU BVH, and gradient and smoothing utilities

Requires Python 3.10 or newer and PyTorch with CUDA support. pip install builds the extension from source, which needs a CUDA toolkit of version 12.4 or newer and a C++ compiler. Prebuilt Linux wheels per CUDA version are attached to the GitHub releases; the installation guide shows how to install them.

pip install isoext

import isoext
from isoext import viewer

grid = isoext.UniformGrid([256, 256, 256])
grid.set_values(grid.get_points().norm(dim=-1) - 0.8)  # Sphere

vertices, faces = isoext.marching_cubes(grid)

server = viewer.show(vertices, faces)  # opens the mesh in the browser
isoext.write_obj("sphere.obj", vertices, faces)

Median extraction times for a sphere SDF on an RTX 5090:

See the performance page for the full table, the method variants, and how to reproduce it.

See the full documentation for guides on grids, extraction methods, and the API reference.

isoext builds on:

• PyTorch — fields and meshes are exchanged as torch tensors
• nanobind — Python bindings for the CUDA core
• Thrust — GPU primitives used throughout the extraction pipeline
• viser — powers the interactive viewer
• scikit-build-core — the build system

Two marching cubes variants adapt existing implementations: the lewiner lookup tables are converted from scikit-image, and the vega variant is a port of MC33_c_library by David Vega (MIT License). The mesh SDF builds and traverses its bounding volume hierarchy with cuBQL by NVIDIA (Apache License 2.0), vendored under ext/cuBQL.

The test meshes of isoext.assets are downloaded from their authors on first use: the bunny, armadillo and dragon from the Stanford Computer Graphics Laboratory (research use), and Spot from Keenan Crane (public domain).

The algorithms themselves come from published papers, cited on each method's documentation page and collected in the references.

MIT License. See LICENSE for details.