It turns raw point clouds into measurable geometric outputs: registration transforms, fitted primitives, clusters, bounding boxes, filtered point sets, and benchmark reports.
ICP uses nearest neighbors as correspondences. If the initial pose is far away, nearest neighbors can be geometrically wrong, so SVD optimizes the wrong pairs and converges to a local minimum.
RANSAC scores models by inlier count under a residual threshold. Outliers can damage individual samples, but repeated random sampling eventually finds a mostly inlier sample when the inlier ratio is sufficient.
For balanced trees, build time is roughly O(N log N). Average nearest-neighbor
query time is often close to O(log N) in low dimensions, but degrades in high
dimensions.
PCA computes covariance eigenvectors. The point cloud is projected into this orthonormal basis, an AABB is computed in PCA coordinates, and the box corners are transformed back to world coordinates.
Point-to-point minimizes Euclidean distance between matched points. Point-to- plane minimizes distance along the target normal, which often converges faster for smooth surfaces because it models local surface geometry.
FPFH descriptors create geometry-based candidate correspondences before local optimization. RANSAC filters those correspondences into a coarse transform, giving ICP a much better starting point.
Benchmarks explain tradeoffs: readable educational implementations are useful, but specialized libraries may be faster. Robust pipelines may cost more runtime but survive noise, outliers, and poor initial poses.