MeshCat.jl: Julia bindings to the MeshCat WebGL viewer

MeshCat is a remotely-controllable 3D viewer, built on top of three.js. The viewer contains a tree of objects and transformations (i.e. a scene graph) and allows those objects and transformations to be added and manipulated with simple commands. This makes it easy to create 3D visualizations of geometries, mechanisms, and robots. MeshCat.jl runs on macOS, Linux, and Windows.

The MeshCat viewer runs entirely in the browser, with no external dependencies. All files are served locally, so no internet connection is required. Communication between the browser and your Julia code is managed by WebIO.jl. That means that MeshCat should work anywhere WebIO works:

In a normal browser tab
Inside a Jupyter Notebook with IJulia.jl
In a standalone window with Blink.jl
Inside the Juno IDE

As much as possible, MeshCat.jl tries to use existing implementations of its fundamental types. In particular, we use:

Geometric primitives and meshes from GeometryTypes.jl
Colors from ColorTypes.jl
Affine transformations from CoordinateTransformations.jl

That means that MeshCat should play well with other tools in the JuliaGeometry ecosystem like MeshIO.jl, Meshing.jl, etc.

Demos

Basic Usage

For detailed examples of usage, check out demo.ipynb.

Animation

To learn about the animation system (introduced in MeshCat.jl v0.2.0), see animation.ipynb.

Related Projects

MeshCat.jl is a successor to DrakeVisualizer.jl, and the interface is quite similar (with the exception that we use setobject! instead of setgeometry!). The primary difference is that DrakeVisualizer required Director, LCM, and VTK, all of which could be difficult to install, while MeshCat just needs a web browser. MeshCat also has better support for materials, textures, point clouds, and complex meshes.

You may also want to check out:

meshcat-python: the Python implementation of the same protocol
MeshCatMechanisms.jl extensions to MeshCat.jl for visualizing mechanisms, robots, and URDFs

Examples

Create a visualizer and open it

using MeshCat
vis = Visualizer()
open(vis)

## In an IJulia/Jupyter notebook, you can also do:
# IJuliaCell(vis)

Cube

using GeometryTypes
using CoordinateTransformations

setobject!(vis, HyperRectangle(Vec(0., 0, 0), Vec(1., 1, 1)))
settransform!(vis, Translation(-0.5, -0.5, 0))

Point Clouds

using ColorTypes
verts = rand(Point3f0, 100_000)
colors = [RGB(p...) for p in verts]
setobject!(vis, PointCloud(verts, colors))

Contours

# Visualize a mesh from the level set of a function
using Meshing
f = x -> sum(sin, 5 * x)
sdf = SignedDistanceField(f, HyperRectangle(Vec(-1, -1, -1), Vec(2, 2, 2)))
mesh = HomogenousMesh(sdf, MarchingTetrahedra())
setobject!(vis, mesh, 
           MeshPhongMaterial(color=RGBA{Float32}(1, 0, 0, 0.5)))

Polyhedra

# Visualize a polyhedron from Polyhedra.jl
using Polyhedra
using CDDLib
# Construct a polyhedron in 4 dimensions
ext1 = SimpleVRepresentation([0 1 2 3;0 2 1 3; 1 0 2 3; 1 2 0 3; 2 0 1 3; 2 1 0 3;
                              0 1 3 2;0 3 1 2; 1 0 3 2; 1 3 0 2; 3 0 1 2; 3 1 0 2;
                              0 3 2 1;0 2 3 1; 3 0 2 1; 3 2 0 1; 2 0 3 1; 2 3 0 1;
                              3 1 2 0;3 2 1 0; 1 3 2 0; 1 2 3 0; 2 3 1 0; 2 1 3 0])
poly1 = CDDPolyhedron{4,Rational{BigInt}}(ext1)

# Project that polyhedron down to 3 dimensions for visualization
poly2 = project(poly1, [1 1 1; -1 1 1; 0 -2 1; 0 0 -3])

# Show the result
setobject!(vis, poly2)

Mechanisms

Using https://github.com/rdeits/MeshCatMechanisms.jl

tkoolen / MeshCat.jl