3 releases
new 0.2.2 | Oct 23, 2024 |
---|---|
0.2.1 | Jun 6, 2024 |
0.2.0 | May 17, 2024 |
#549 in Machine learning
Used in f3l
140KB
3K
SLoC
F3l Features
Data Features.
Bounding
- AABB
- OBB
let obb = OBB::compute(&vertices);
// Get OBB 8 corners
let p0 = obb.center
- obb.primary * obb.length[0]
- obb.secondary * obb.length[1]
- obb.tertiary * obb.length[2];
let p1 = p0 + obb.primary * obb.length[0] * 2.;
let p2 = p0 + obb.secondary * obb.length[1] * 2.;
let p3 = p0 + obb.tertiary * obb.length[2] * 2.;
let p4 = p2 + obb.primary * obb.length[0] * 2.;
let p5 = p1 + obb.tertiary * obb.length[2] * 2.;
let p6 = p2 + obb.tertiary * obb.length[2] * 2.;
let p7 = p4 + obb.tertiary * obb.length[2] * 2.;
Normal Estimate
- For each point search neighbors.
- Compute eigenvector of neighbors.
- The smallest eigenvalue one is which normal.
Normal Search Method [KDTree]
// Radius
let mut estimator = NormalEstimation::new(SearchBy::Radius(0.08f32));
// KNN
let mut estimator = NormalEstimation::new(SearchBy::Count(10));
// Compute!
if !estimator.compute(&vertices) {
println!("Compute Normal Failed. Exit...");
return;
}
let normals = estimator.normals();
Dependencies
~5–12MB
~260K SLoC