fast-simplification
Wrapper around the Fast-Quadric-Mesh-Simplification library.
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, supports current Python versions (3.9–3.14), and offers a performant alternative to VTK decimation with straightforward numpy and PyVista integration. Install friction is moderate but acceptable for a compiled extension. MIT licensing removes legal barriers.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy arrays; PyVista integration is optional but recommended for mesh I/O.
- Medium install friction due to compiled wheels for multiple Python versions and platforms (cp310–cp313 on macOS, Linux, Windows).
- Active maintenance with a recent release (2 days old) and steady repository activity (206 stars).
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.
last release 2026-08-12 (2 days) · last repo commit 2026-08-12 · 206 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 602,214 downloads/mo, #5,816 on PyPI
Alternatives
Verify before relying
import numpy as np
import fast_simplification
points = np.array([[0.5, -0.5, 0.0], [0.0, -0.5, 0.0], [-0.5, -0.5, 0.0]])
faces = np.array([[0, 1, 2]])
points_out, faces_out = fast_simplification.simplify(points, faces, target_reduction=0.5)- Whether the 4–5x performance improvement over VTK decimation mentioned in the description is consistently reproducible across different mesh types and sizes.
- Specific memory requirements or limits for large meshes during simplification.
What it is and what it does
fast-simplification wraps the Fast-Quadric-Mesh-Simplification C++ library to reduce 3D mesh complexity in Python. It takes arrays of vertex points and triangle faces, then outputs a simplified mesh with fewer triangles while maintaining the overall shape. The package works directly with numpy arrays or integrates with PyVista for seamless mesh file I/O and visualization.
The library is designed for applications that need fast mesh decimation—such as preparing models for real-time rendering, reducing storage size, or processing large mesh collections with consistent topology. It offers both a basic numpy-based API and an advanced PyVista integration, plus a replay mechanism to apply the same decimation sequence to multiple meshes or to revert to intermediate simplification levels without recomputing from scratch.
Use it for
- Reduce polygon count on 3D models for real-time graphics or game engines.
- Prepare high-resolution scans or CAD meshes for web or mobile deployment.
- Apply identical decimation to a collection of meshes that share topology (e.g., anatomical variants).
- Speed up mesh processing pipelines by simplifying before further analysis or rendering.
- Transfer field data between original and simplified meshes using vertex correspondence maps.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, supports current Python versions (3.9–3.14), and offers a performant alternative to VTK decimation with straightforward numpy and PyVista integration. Install friction is moderate but acceptable for a compiled extension. MIT licensing removes legal barriers.
Install
fast-simplification on PyPI
Before you install
Medium install friction due to compiled wheels for multiple Python versions and platforms (cp310–cp313 on macOS, Linux, Windows). Active maintenance with a recent release (2 days old) and steady repository activity (206 stars). Single runtime dependency on numpy.
Requires numpy arrays; PyVista integration is optional but recommended for mesh I/O.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.
Quickstart
import numpy as np
import fast_simplification
points = np.array([[0.5, -0.5, 0.0], [0.0, -0.5, 0.0], [-0.5, -0.5, 0.0]])
faces = np.array([[0, 1, 2]])
points_out, faces_out = fast_simplification.simplify(points, faces, target_reduction=0.5)Verify before relying
- Whether the 4–5x performance improvement over VTK decimation mentioned in the description is consistently reproducible across different mesh types and sizes.
- Specific memory requirements or limits for large meshes during simplification.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 2 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 602,214 / month, #5,816 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: fast_simplification-0.2.0-cp310-cp310-macosx_10_14_x86_64.whl; fast_simplification-0.2.0-cp310-cp310-macosx_11_0_arm64.whl; fast_simplification-0.2.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; fast_simplification-0.2.0-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; fast_simplification-0.2.0-cp310-cp310-win_amd64.whl; fast_simplification-0.2.0-cp311-cp311-macosx_10_14_x86_64.whl; fast_simplification-0.2.0-cp311-cp311-macosx_11_0_arm64.whl; fast_simplification-0.2.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; fast_simplification-0.2.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; fast_simplification-0.2.0-cp311-cp311-win_amd64.whl; fast_simplification-0.2.0-cp312-cp312-macosx_10_14_x86_64.whl; fast_simplification-0.2.0-cp312-cp312-macosx_11_0_arm64.whl; fast_simplification-0.2.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; fast_simplification-0.2.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; fast_simplification-0.2.0-cp312-cp312-win_amd64.whl; fast_simplification-0.2.0-cp313-cp313-macosx_10_14_x86_64.whl; fast_simplification-0.2.0-cp313-cp313-macosx_11_0_arm64.whl; fast_simplification-0.2.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; fast_simplification-0.2.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; fast_simplification-0.2.0-cp313-cp313-win_amd64.whl
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