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Python library for loading and using triangular meshes.

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Geometry Processing Library for Python

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The public CGAL repository, see the README below

Quick Overview

Trimesh is a Python library for loading, manipulating, and analyzing 3D triangular meshes. It provides a wide range of functionality for working with 3D geometry, including mesh processing, collision detection, and path planning. Trimesh is designed to be efficient and easy to use, making it suitable for both research and practical applications.

Pros

  • Comprehensive set of tools for 3D mesh manipulation and analysis
  • Efficient implementation, suitable for large-scale mesh processing
  • Good integration with other scientific Python libraries (NumPy, SciPy)
  • Extensive documentation and examples

Cons

  • Steep learning curve for beginners in 3D geometry
  • Some advanced features may require additional dependencies
  • Limited support for non-triangular meshes
  • Performance can be slower compared to specialized C++ libraries for certain operations

Code Examples

Loading and visualizing a mesh:

import trimesh
import numpy as np

# Load a mesh from an STL file
mesh = trimesh.load('example.stl')

# Visualize the mesh
mesh.show()

Performing boolean operations:

# Create two simple meshes
sphere = trimesh.primitives.Sphere(radius=1.0)
box = trimesh.primitives.Box(extents=[2, 2, 2])

# Perform boolean intersection
intersection = trimesh.boolean.intersection([sphere, box])

# Visualize the result
intersection.show()

Computing mesh properties:

# Load a mesh
mesh = trimesh.load('example.obj')

# Compute various properties
print(f"Volume: {mesh.volume}")
print(f"Surface area: {mesh.area}")
print(f"Center of mass: {mesh.center_mass}")
print(f"Moment of inertia: {mesh.moment_inertia}")

Getting Started

To get started with Trimesh, follow these steps:

  1. Install Trimesh using pip:

    pip install trimesh[easy]
    
  2. Import the library in your Python script:

    import trimesh
    
  3. Load a mesh from a file or create a primitive shape:

    # Load from file
    mesh = trimesh.load('path/to/your/mesh.stl')
    
    # Or create a primitive
    sphere = trimesh.primitives.Sphere(radius=1.0)
    
  4. Start manipulating and analyzing your mesh:

    # Compute some properties
    print(f"Mesh volume: {mesh.volume}")
    print(f"Mesh surface area: {mesh.area}")
    
    # Visualize the mesh
    mesh.show()
    

Competitor Comparisons

9,923

Point Cloud Library (PCL)

Pros of PCL

  • Extensive functionality for point cloud processing, including advanced algorithms for segmentation, registration, and surface reconstruction
  • Large and active community, with extensive documentation and support
  • Optimized for performance, with GPU acceleration for certain operations

Cons of PCL

  • Steeper learning curve due to its complexity and extensive API
  • Heavier resource requirements, which may impact performance on less powerful systems
  • C++ based, which may be less accessible for users more comfortable with Python

Code Comparison

PCL (C++):

#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::VoxelGrid<pcl::PointXYZ> vg;
vg.setInputCloud (cloud);
vg.setLeafSize (0.01f, 0.01f, 0.01f);
vg.filter (*cloud_filtered);

Trimesh (Python):

import trimesh

mesh = trimesh.load('model.stl')
mesh = mesh.simplify_quadric_decimation(face_count=1000)
mesh.export('simplified_model.stl')
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3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)

Pros of PyVista

  • More comprehensive visualization capabilities, including 3D plotting and interactive rendering
  • Extensive documentation and examples for various use cases
  • Seamless integration with VTK for advanced visualization features

Cons of PyVista

  • Steeper learning curve due to its broader feature set
  • Larger dependency footprint, which may impact installation and deployment

Code Comparison

PyVista:

import pyvista as pv
mesh = pv.Sphere()
plotter = pv.Plotter()
plotter.add_mesh(mesh)
plotter.show()

Trimesh:

import trimesh
mesh = trimesh.creation.icosphere()
mesh.show()

Key Differences

  • PyVista focuses on scientific visualization and data analysis, while Trimesh specializes in 3D mesh processing and manipulation
  • PyVista offers more advanced plotting capabilities, whereas Trimesh excels in mesh operations and geometric computations
  • Trimesh has a lighter footprint and simpler API, making it easier to get started for basic mesh operations

Both libraries have their strengths, and the choice between them depends on the specific requirements of your project. PyVista is better suited for complex visualizations and data analysis, while Trimesh is ideal for efficient mesh processing and manipulation tasks.

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Geometry Processing Library for Python

Pros of PyMesh

  • More comprehensive geometry processing capabilities, including advanced mesh operations and boolean operations
  • Supports a wider range of file formats for import and export
  • Provides bindings to external libraries for additional functionality

Cons of PyMesh

  • Steeper learning curve due to more complex API
  • Less active development and community support
  • Requires compilation and external dependencies, making installation more challenging

Code Comparison

PyMesh:

import pymesh
mesh = pymesh.load_mesh("input.obj")
mesh = pymesh.subdivide(mesh, order=1)
pymesh.save_mesh("output.obj", mesh)

Trimesh:

import trimesh
mesh = trimesh.load("input.obj")
mesh = mesh.subdivide()
mesh.export("output.obj")

Key Differences

  • PyMesh offers more advanced geometry processing features but has a steeper learning curve
  • Trimesh is easier to install and use, with a more Pythonic API
  • PyMesh supports more file formats, while Trimesh focuses on common formats
  • Trimesh has more active development and community support
  • PyMesh provides bindings to external libraries, while Trimesh is more self-contained

Both libraries have their strengths, and the choice depends on specific project requirements and user preferences.

4,934

The public CGAL repository, see the README below

Pros of CGAL

  • More comprehensive and feature-rich library for computational geometry
  • Supports a wider range of geometric algorithms and data structures
  • Better suited for complex geometric computations and research applications

Cons of CGAL

  • Steeper learning curve due to its complexity and extensive API
  • Heavier and slower to compile compared to Trimesh
  • Less focused on 3D mesh processing specifically

Code Comparison

CGAL example (C++):

#include <CGAL/Exact_predicates_inexact_constructions_kernel.h>
#include <CGAL/Delaunay_triangulation_3.h>

typedef CGAL::Exact_predicates_inexact_constructions_kernel K;
typedef CGAL::Delaunay_triangulation_3<K> Delaunay;

Trimesh example (Python):

import trimesh

mesh = trimesh.load('model.stl')
convex_hull = mesh.convex_hull

CGAL offers a more low-level, C++ based approach with fine-grained control over geometric operations, while Trimesh provides a higher-level, Python-based interface focused on 3D mesh manipulation.

Trimesh is more accessible for quick 3D mesh operations and analysis, whereas CGAL is better suited for advanced geometric computations and algorithm development. The choice between the two depends on the specific requirements of the project and the user's familiarity with the respective programming languages and APIs.

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README

trimesh


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Trimesh is a pure Python 3.7+ library for loading and using triangular meshes with an emphasis on watertight surfaces. The goal of the library is to provide a full featured and well tested Trimesh object which allows for easy manipulation and analysis, in the style of the Polygon object in the Shapely library.

The API is mostly stable, but this should not be relied on and is not guaranteed: install a specific version if you plan on deploying something using trimesh.

Pull requests are appreciated and responded to promptly! If you'd like to contribute, here is an up to date list of potential enhancements although things not on that list are also welcome. Here's a quick development and contributing guide.

Basic Installation

Keeping trimesh easy to install is a core goal, thus the only hard dependency is numpy. Installing other packages adds functionality but is not required. For the easiest install with just numpy, pip can generally install trimesh cleanly on Windows, Linux, and OSX:

pip install trimesh

The minimal install can load many supported formats (STL, PLY, GLTF/GLB) into numpy arrays. More functionality is available when soft dependencies are installed. This includes things like convex hulls (scipy), graph operations (networkx), faster ray queries (embreex), vector path handling (shapely and rtree), XML formats like 3DXML/XAML/3MF (lxml), preview windows (pyglet), faster cache checks (xxhash), etc. To install trimesh with the soft dependencies that generally install cleanly on Linux, OSX, and Windows using pip:

pip install trimesh[easy]

Further information is available in the advanced installation documentation.

Quick Start

Here is an example of loading a mesh from file and colorizing its faces. Here is a nicely formatted ipython notebook version of this example. Also check out the cross section example.

import numpy as np
import trimesh

# attach to logger so trimesh messages will be printed to console
trimesh.util.attach_to_log()

# mesh objects can be created from existing faces and vertex data
mesh = trimesh.Trimesh(vertices=[[0, 0, 0], [0, 0, 1], [0, 1, 0]],
                       faces=[[0, 1, 2]])

# by default, Trimesh will do a light processing, which will
# remove any NaN values and merge vertices that share position
# if you want to not do this on load, you can pass `process=False`
mesh = trimesh.Trimesh(vertices=[[0, 0, 0], [0, 0, 1], [0, 1, 0]],
                       faces=[[0, 1, 2]],
                       process=False)

# some formats represent multiple meshes with multiple instances
# the loader tries to return the datatype which makes the most sense
# which will for scene-like files will return a `trimesh.Scene` object.
# if you *always* want a straight `trimesh.Trimesh` you can ask the
# loader to "force" the result into a mesh through concatenation
mesh = trimesh.load('models/CesiumMilkTruck.glb', force='mesh')

# mesh objects can be loaded from a file name or from a buffer
# you can pass any of the kwargs for the `Trimesh` constructor
# to `trimesh.load`, including `process=False` if you would like
# to preserve the original loaded data without merging vertices
# STL files will be a soup of disconnected triangles without
# merging vertices however and will not register as watertight
mesh = trimesh.load('../models/featuretype.STL')

# is the current mesh watertight?
mesh.is_watertight

# what's the euler number for the mesh?
mesh.euler_number

# the convex hull is another Trimesh object that is available as a property
# lets compare the volume of our mesh with the volume of its convex hull
print(mesh.volume / mesh.convex_hull.volume)

# since the mesh is watertight, it means there is a
# volumetric center of mass which we can set as the origin for our mesh
mesh.vertices -= mesh.center_mass

# what's the moment of inertia for the mesh?
mesh.moment_inertia

# if there are multiple bodies in the mesh we can split the mesh by
# connected components of face adjacency
# since this example mesh is a single watertight body we get a list of one mesh
mesh.split()

# facets are groups of coplanar adjacent faces
# set each facet to a random color
# colors are 8 bit RGBA by default (n, 4) np.uint8
for facet in mesh.facets:
    mesh.visual.face_colors[facet] = trimesh.visual.random_color()

# preview mesh in an opengl window if you installed pyglet and scipy with pip
mesh.show()

# transform method can be passed a (4, 4) matrix and will cleanly apply the transform
mesh.apply_transform(trimesh.transformations.random_rotation_matrix())

# axis aligned bounding box is available
mesh.bounding_box.extents

# a minimum volume oriented bounding box also available
# primitives are subclasses of Trimesh objects which automatically generate
# faces and vertices from data stored in the 'primitive' attribute
mesh.bounding_box_oriented.primitive.extents
mesh.bounding_box_oriented.primitive.transform

# show the mesh appended with its oriented bounding box
# the bounding box is a trimesh.primitives.Box object, which subclasses
# Trimesh and lazily evaluates to fill in vertices and faces when requested
# (press w in viewer to see triangles)
(mesh + mesh.bounding_box_oriented).show()

# bounding spheres and bounding cylinders of meshes are also
# available, and will be the minimum volume version of each
# except in certain degenerate cases, where they will be no worse
# than a least squares fit version of the primitive.
print(mesh.bounding_box_oriented.volume,
      mesh.bounding_cylinder.volume,
      mesh.bounding_sphere.volume)

Features

  • Import meshes from binary/ASCII STL, Wavefront OBJ, ASCII OFF, binary/ASCII PLY, GLTF/GLB 2.0, 3MF, XAML, 3DXML, etc.
  • Import and export 2D or 3D vector paths from/to DXF or SVG files
  • Import geometry files using the GMSH SDK if installed (BREP, STEP, IGES, INP, BDF, etc)
  • Export meshes as binary STL, binary PLY, ASCII OFF, OBJ, GLTF/GLB 2.0, COLLADA, etc.
  • Export meshes using the GMSH SDK if installed (Abaqus INP, Nastran BDF, etc)
  • Preview meshes using pyglet or in- line in jupyter notebooks using three.js
  • Automatic hashing of numpy arrays for change tracking using MD5, zlib CRC, or xxhash
  • Internal caching of computed values validated from hashes
  • Calculate face adjacencies, face angles, vertex defects, etc.
  • Calculate cross sections, i.e. the slicing operation used in 3D printing
  • Slice meshes with one or multiple arbitrary planes and return the resulting surface
  • Split mesh based on face connectivity using networkx, graph-tool, or scipy.sparse
  • Calculate mass properties, including volume, center of mass, moment of inertia, principal components of inertia vectors and components
  • Repair simple problems with triangle winding, normals, and quad/tri holes
  • Convex hulls of meshes
  • Compute rotation/translation/tessellation invariant identifier and find duplicate meshes
  • Determine if a mesh is watertight, convex, etc.
  • Uniformly sample the surface of a mesh
  • Ray-mesh queries including location, triangle index, etc.
  • Boolean operations on meshes (intersection, union, difference) using Manifold3D or Blender Note that mesh booleans in general are usually slow and unreliable
  • Voxelize watertight meshes
  • Volume mesh generation (TETgen) using Gmsh SDK
  • Smooth watertight meshes using laplacian smoothing algorithms (Classic, Taubin, Humphrey)
  • Subdivide faces of a mesh
  • Approximate minimum volume oriented bounding boxes for meshes
  • Approximate minimum volume bounding spheres
  • Calculate nearest point on mesh surface and signed distance
  • Determine if a point lies inside or outside of a well constructed mesh using signed distance
  • Primitive objects (Box, Cylinder, Sphere, Extrusion) which are subclassed Trimesh objects and have all the same features (inertia, viewers, etc)
  • Simple scene graph and transform tree which can be rendered (pyglet window, three.js in a jupyter notebook, pyrender) or exported.
  • Many utility functions, like transforming points, unitizing vectors, aligning vectors, tracking numpy arrays for changes, grouping rows, etc.

Viewer

Trimesh includes an optional pyglet based viewer for debugging and inspecting. In the mesh view window, opened with mesh.show(), the following commands can be used:

  • mouse click + drag rotates the view
  • ctl + mouse click + drag pans the view
  • mouse wheel zooms
  • z returns to the base view
  • w toggles wireframe mode
  • c toggles backface culling
  • g toggles an XY grid with Z set to lowest point
  • a toggles an XYZ-RGB axis marker between: off, at world frame, or at every frame and world, and at every frame
  • f toggles between fullscreen and windowed mode
  • m maximizes the window
  • q closes the window

If called from inside a jupyter notebook, mesh.show() displays an in-line preview using three.js to display the mesh or scene. For more complete rendering (PBR, better lighting, shaders, better off-screen support, etc) pyrender is designed to interoperate with trimesh objects.

Projects Using Trimesh

You can check out the Github network for things using trimesh. A select few:

  • Nvidia's kaolin for deep learning on 3D geometry.
  • Cura, a popular slicer for 3D printing.
  • Berkeley's DexNet4 and related ambidextrous.ai work with robotic grasp planning and manipulation.
  • Kerfed's Kerfed's Engine for analyzing assembly geometry for manufacturing.
  • MyMiniFactory's P2Slice for preparing models for 3D printing.
  • pyrender A library to render scenes from Python using nice looking PBR materials.
  • urdfpy Load URDF robot descriptions in Python.
  • moderngl-window A helper to create GL contexts and load meshes.
  • vedo Visualize meshes interactively (see example gallery).
  • FSLeyes View MRI images and brain data.

Which Mesh Format Should I Use?

Quick recommendation: GLB or PLY. Every time you replace OBJ with GLB an angel gets its wings.

If you want things like by-index faces, instancing, colors, textures, etc, GLB is a terrific choice. GLTF/GLB is an extremely well specified modern format that is easy and fast to parse: it has a JSON header describing data in a binary blob. It has a simple hierarchical scene graph, a great looking modern physically based material system, support in dozens-to-hundreds of libraries, and a John Carmack endorsment. Note that GLTF is a large specification, and trimesh only supports a subset of features: loading basic geometry is supported, NOT supported are fancier things like animations, skeletons, etc.

In the wild, STL is perhaps the most common format. STL files are extremely simple: it is basically just a list of triangles. They are robust and are a good choice for basic geometry. Binary PLY files are a good step up, as they support indexed faces and colors.

Wavefront OBJ is also pretty common: unfortunately OBJ doesn't have a widely accepted specification so every importer and exporter implements things slightly differently, making it tough to support. It also allows unfortunate things like arbitrary sized polygons, has a face representation which is easy to mess up, references other files for materials and textures, arbitrarily interleaves data, and is slow to parse. Give GLB or PLY a try as an alternative!

How can I cite this library?

A question that comes up pretty frequently is how to cite the library. A quick BibTex recommendation:

@software{trimesh,
	author = {{Dawson-Haggerty et al.}},
	title = {trimesh},
	url = {https://trimesh.org/},
	version = {3.2.0},
	date = {2019-12-8},
}

Containers

If you want to deploy something in a container that uses trimesh automated debian:slim-bullseye based builds with trimesh and most dependencies are available on Docker Hub with image tags for latest, git short hash for the commit in main (i.e. trimesh/trimesh:0c1298d), and version (i.e. trimesh/trimesh:3.5.27):

docker pull trimesh/trimesh

Here's an example of how to render meshes using LLVMpipe and XVFB inside a container.