This Julia package is a lightweight framework for defining N-Dimensional region trees. In 2D, these are called *region quadtrees*, and in 3D they are typically referred to as *octrees*. A region tree is a simple data structure used to describe some kind of spatial data with varying resolution. Each element in the tree can be a leaf, representing an N-dimensional rectangle of space, or a node which is divided exactly in half along each axis into 2^N children. In addition, each element in a `RegionTrees.jl`

tree can carry an arbitrary data payload. This makes it easy to use `RegionTrees`

to approximate functions or describe other interesting spatial data.

- Lightweight code with few dependencies (only
`StaticArrays.jl`

and`Iterators.jl`

are required) - Optimized for speed and for few memory allocations
- Liberal use of
`@generated`

functions lets us unroll most loops and prevent allocating temporary arrays

- Liberal use of
- Built-in support for general adaptive sampling techniques

See examples/demo/demo.ipynb for a tour through the API. You can also check out:

- examples/adaptive_distance_fields/adaptive_distances.ipynb for an adaptively-sampled distance field, or AdaptiveDistanceFields.jl for a more complete example [1]
- examples/adaptive_mpc/adaptive_mpc.ipynb for an adaptive approximation of a model-predictive controller

[1] Frisken et al. "Adaptively Sampled Distance Fields: A General Representation of Shape for Computer Graphics". SIGGRAPH 2000.

An adaptively sampled distance field, from `examples/adaptive_distances.ipynb`

:

An adaptively sampled model-predictive control problem, from `examples/adaptive_mpc.ipynb`

:

An adaptive distance field in 3D, from AdaptiveDistanceFields.jl:

12/03/2016

2 days ago

87 commits