Installation

The only thing required to install trimesh is numpy.

All other dependencies are ‘soft,’ or trimesh will raise the exceptions (usually but not always an ImportError) at runtime if a function is called that requires a package that isn’t installed. If you do the most basic install of trimesh it will only install numpy:

pip install trimesh

This will enable you to load most formats into numpy arrays: STL, PLY, OBJ, GLB, GLTF.

If you’d like most soft dependencies which should install cleanly on Mac, Windows, and Linux, you can use the easy pip extra:

pip install trimesh[easy]

Or if you want the full experience, you can try the all extra, where packages may only be available for Linux:

pip install trimesh[all]

Conda Packages

If you prefer a conda environment, trimesh is available on conda-forge (trimesh-feedstock repo)

If you install Miniconda you can then run:

conda install -c conda-forge trimesh

Ubuntu-Debian Notes

Blender and openSCAD are soft dependencies used for boolean operations with subprocess, you can get them with apt:

sudo apt-get install openscad blender

Dependency Overview

Trimesh has a lot of soft-required upstream packages. We try to make sure they’re active and big-ish. Here’s a quick summary of what they’re used for.

Package	Description	Alternatives	Level
numpy	The base container for fast array types.	None.	required
scipy	Provides convex hulls (scipy.spatial.ConvexHull), fast graph operations (scipy.sparse.csgraph), fast nearest-point queries (scipy.spatial.cKDTree), b-spline evaluation (scipy.interpolate).	Nothing comes to mind, it does a whole heck of a lot.	easy
lxml	Parse XML documents. We use this over the built-in ones as it was slightly faster, and there was a format implemented which was extremely annoying to handle without the ability to get parent nodes (which lxml has but built-in XML doesn’t).	Standard library’s XML	easy
networkx	A nice-to-use pure Python graph library that’s faster than you’d think. It implements DFS, BFS, and the usual FAANG-interview-question algorithms. A lot of the commonly run stuff in trimesh has been re-written to use scipy.sparse.csgraph as it’s also an easy install and is way faster in most cases. But if you have a small-ish graph the API for networkx is way easier to “grok”.	graph-tool, scipy.sparse.csgraph	easy
shapely	Bindings to GEOS for 2D spatial stuff: “set-theoretic analysis and manipulation of planar features.” It lets you offset, union, and query polygons nicely.	clipper maybe?	easy
rtree	Query ND rectangles with a spatial tree for a “broad phase” intersection. Used in polygon generation (“given N closed curves which curve contains the other curve?”) and as the broad-phase for the built-in-numpy slow ray query engine.	fcl maybe?	easy
requests	Do network queries in trimesh.exchange.load_remote, will only make network requests when asked		easy
sympy	Evaluate symbolic algebra		easy
xxhash	Quickly hash arrays, used for our cache checking		easy
msgpack	A serialization method that supports bytes-blobs.	protobuf	easy
chardet	When we fail to decode text as UTF-8 we then check with chardet which guesses an encoding. This lets us load files even with weird encodings.		easy
colorlog	Printing logs with colors.		easy
pillow	Reading raster images for textures, and rendering polygons into raster images.		easy
svg.path	Parsing SVG path strings.		easy
jsonschema	Validating our exports for formats like GLTF.		easy
pycollada	Parse dae files.		easy
pyglet	OpenGL bindings for our simple debug viewer.		easy
xatlas	Unwrap meshes to generate UV coordinates quickly and well.		all
python-fcl	Do collision queries between meshes		all
glooey	Provide a viewer with widgets.		all
meshio	Load additional mesh formats.		all
scikit-image	Used in voxel ops		all
triangle	Triangulate 2D polygons, with somewhat unclear licensing.	mapbox-earcut	all
mapbox-earcut	Triangulate 2D polygons, with a permissive license but mostly abandoned	triangle	all
psutil	Get current memory usage, useful for checking to see if we’re going to run out of memory instantiating a giant array		all