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 which includes all the testing and recommended packages:

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

Dependency Overview#

Trimesh has a lot of soft-required upstream packages, and we try to make sure they’re actively maintained. Here’s a quick summary of what they’re used for:

Package	Description	Alternatives	Level
`numpy`	The base container for fast array types.		`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`).		`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`	Pure Python graph library that’s reasonably fast and has a nice API. `scipy.sparse.csgraph` is way faster in most cases but is hard to understand and doesn’t implement as many algorithms.	`graph-tool`, `scipy.sparse.csgraph`	`easy`
`shapely`	Bindings to `GEOS` for 2D spatial stuff: “set-theoretic analysis and manipulation of planar features” which lets you offset, union, and query polygons.	`clipper`	`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`
`httpx`	Do network queries in `trimesh.exchange.load_remote`, will only make network requests when asked	`requests`, `aiohttp`	`easy`
`sympy`	Evaluate symbolic algebra		`recommend`
`xxhash`	Quickly hash arrays, used for our cache checking		`easy`
`chardet`	When we fail to decode text as UTF-8 we then check with chardet which guesses an encoding, letting us load files even with weird encodings.		`easy`
`colorlog`	Printing logs with colors.		`easy`
`pillow`	Reading raster images for textures and render 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<2`	OpenGL bindings for our simple debug viewer.		`recommend`
`xatlas`	Unwrap meshes to generate UV coordinates quickly and well.		`recommend`
`python-fcl`	Do collision queries between meshes		`recommend`
`glooey`	Provide a viewer with widgets.		`recommend`
`meshio`	Load additional mesh formats.		`recommend`
`scikit-image`	Used in voxel ops		`recommend`
`mapbox-earcut`	Triangulate 2D polygons	`triangle` which has an unusual license	`easy`
`psutil`	Get current memory usage, useful for checking to see if we’re going to run out of memory instantiating a giant array		`recommend`
`ruff`	A static code analyzer and formatter that replaces `flake8` and `black`.	`flake8`	`test`
`pytest`	A test runner.		`test`
`pytest-cov`	A plugin to calculate test coverage.		`test`
`pyinstrument`	A sampling based profiler for performance tweaking.		`test`
`vhacdx`	A binding for VHACD which provides convex decompositions		`recommend`
`manifold3d`	A binding for the Manifold mesh boolean engine		`recommend`
`openctm`	A binding for OpenCTM loaders enabling `.ctm` loading		`recommend`
`cascadio`	A binding for OpenCASCADE enabling `.STEP` loading		`recommend`

Adding A Dependency#

If there’s no way to implement something reasonably in vectorized Python or there is a mature minimal C++ or Rust implementation of something useful and complicated we may add a dependency. If it’s a major, active project with few dependencies (i.e. jinja2) that’s probably fine. Otherwise it’s a lot more of a commitment than just implementing the function in Python however. An example of this is embree, Intel’s ray check engine: it is a super complicated thing to do well and 50-100x faster than Python ray checks.

There are a few projects that we’ve forked into the trimesh GitHub organization which you can take a look at. The general idea of the requirements for a new compiled dependency are:

is actively maintained and has an MIT/BSD compatible license.
has all source code in the repository or as a submodule, i.e. no mysterious binary blobs.
binding preferably uses pybind11, nanobind or maturin/py03 for Rust projects. Cython is also OK but other options are preferable if possible.
uses cibuildwheel to publish releases configured in pyproject.toml.
has unit tests which run in CI
has minimal dependencies: ideally only numpy.