Deformable surface simulation and visualisation library
Documentation: http://feltech.github.io/Felt/
Source: http://feltech.github.io/Felt/
Video (via UrFelt): https://www.youtube.com/watch?v=KwpXzBb6Nzs
Felt is an implementation of the sparse field level set method for deformable surfaces, and the marching cubes method of polygonising them.
The surface is embedded in a grid, where each grid node stores its distance to the surface. Thus the surface can be extracted from the points where the distance is zero - it is an implicit surface representation.
Felt makes heavy use of spatial partitioning to reduce memory footprint, provide localised deformations and repolygonisation, and aid in parallelism.
Felt leverages C++14 and is header-only, with a dependencies on boost and Eigen libraries.
Features
- Level set isosurface with arbitrary spatial dimension. Create 2D, 3D or mind bending n-dimensional surfaces.
- 2D and 3D polygonisations (lines and triangles, respectively), with iterative updates as the surface evolves.
- Mutate the surface with lambdas, or any other functor, which are inlined and automatically parallelised.
- Common numerical functions for simple spatial derivatives.
- Serialisation to/from arbitrary output/input streams.
- Spatially partitioned for low memory usage and fast updates.
- OpenMP parallelism for both surface updates and polygonisation.
Future
The library is in a stable state, but the documentation needs work.
There are Doxygen annotations for almost all functions and classes, and quite extensive test cases. I aim to finish tidying up the test cases, which now use the awesome Catch BDD style testing library, and use these as integral parts of the documentation via snippets.
There is ongoing development to support the physics engine Bullet via the amazing game engine Urho3D in my test-bed demo app UrFelt. I aim to move the Bullet and Urho3D classes developed there to form an optional part of the main Felt project.
Example
A super simple example from the tests:
GIVEN("a 2-layer 2D surface in a 9x9 isogrid with 3x3 partitions")
{
// 2D surface with 2x narrow band layers, respectively.
using SurfaceType = Surface<2, 2>;
// Construct the surface.
SurfaceType surface(Vec2i(9, 9), Vec2i(3, 3));
WHEN("a singularity seed is created at the centre")
{
// Create seed point in the centre
surface.seed(Vec2i(0, 0));
WHEN("we expand by 0.6")
{
surface.update([](const auto& pos_, const auto& isogrid_) {
(void)pos_; (void)isogrid_;
return -0.6f;
});
THEN("the grid data and layers are as expected")
{
isogrid_check.data() = {
3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 2.4, 3, 3, 3, 3,
3, 3, 3, 2.4, 1.4, 2.4, 3, 3, 3,
3, 3, 2.4, 1.4, 0.4, 1.4, 2.4, 3, 3,
3, 2.4, 1.4, 0.4, -0.6, 0.4, 1.4, 2.4, 3,
3, 3, 2.4, 1.4, 0.4, 1.4, 2.4, 3, 3,
3, 3, 3, 2.4, 1.4, 2.4, 3, 3, 3,
3, 3, 3, 3, 2.4, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3
};
// snapshot() copies the spatially partitioned grid into a single
// unpartitioned grid. array() returns an Eigen vector with the grid data, giving
// access to arithmetic and other BLAS functions.
isogrid_check.array() =
isogrid_check.array() - surface.isogrid().snapshot()->array();
const Distance diff = isogrid_check.array().sum();
CHECK(diff == Approx(0));