Non-rigid Iterative Closest Point (N-ICP)

[daniel-unyi-42]

Dániel Unyi GSoC 2023 Submission

Project summary:

The package Non-rigid Iterative Closest Point (N-ICP) implements an algorithm which finds a sequence of transformations that aligns a source surface to a target surface. The algorithm currently minimizes four energy terms: point-to-point matching, point-to-plane matching, global rigidity, and local rigidity, which are all quadratic and therefore can be minimized by solving a system of linear equations. For further reference, see paper 1 and paper 2. The implementation uses the Eigen library extensively; it solves the system with the LeastSquaresGradient solver. We optimized the code to run as quick as possible. The source surface is required to be a mesh, whereas the target surface can be a point set with normals. Moreover, the user can specify an std::map which contains ground-truth correspondences.

Commit log:

CGAL/cgal-public-dev@7fba8e04a9d (HEAD -> gsoc2023-nonrigid_icp-danielunyi, origin/gsoc2023-nonrigid_icp-danielunyi) arap modifications after discussion
CGAL/cgal-public-dev@e4d5903600c Cleaning up
CGAL/cgal-public-dev@5cebe76ec12 Nonrigid ICP least squares version - Point-to-plane matching, incorporating arap rotations
CGAL/cgal-public-dev@64ffea3c468 Nonrigid ICP least squares version - minor fixes
CGAL/cgal-public-dev@07ba046b9d2 Nonrigid ICP least squares version
CGAL/cgal-public-dev@98ebe293445 Incorporate ARAP (full rotation matrices)
CGAL/cgal-public-dev@bcf5c2f603f as-rigid-as-possible original paper - python version
CGAL/cgal-public-dev@706bdf3eba5 Drawing correspondences
CGAL/cgal-public-dev@c3f56b4bf27 Large weights on corresponding points
CGAL/cgal-public-dev@22e220b0b48 OpenGR support
CGAL/cgal-public-dev@46da4f862de User can pass point-to-point correspondences as constraints to the registration function
CGAL/cgal-public-dev@9dfdc5c6290 A is set from triplets (slightly faster than the previous version)
CGAL/cgal-public-dev@79a1f586058 Reading in Tosca meshes; user can specify number of threads
CGAL/cgal-public-dev@415fbac64a4 Faster solving with Eigen's analyzePattern
CGAL/cgal-public-dev@2424a1ee91c Target doesn't have to be mesh, point set is sufficient
CGAL/cgal-public-dev@d7648ca20a7 As-rigid-as-possible registration, other minor modifications
CGAL/cgal-public-dev@5bcc2c98927 Mesh Laplacian calculation
CGAL/cgal-public-dev@18deddfbe48 Point-to-plane error + checking error convergence in rigid registration
CGAL/cgal-public-dev@1828f4afbff Registration with Eigen example - the rigid part
CGAL/cgal-public-dev@e7adb5ee294 ICP minimization calculations
CGAL/cgal-public-dev@3f513a6fd0b Using vectorized operations for arap registration
CGAL/cgal-public-dev@78bd2c8ca13 As-rigid-as possible registration in Python
CGAL/cgal-public-dev@5f24ae9d236 Clarify python versions
CGAL/cgal-public-dev@954bc8b1d1f Rigid registration in Python: point-to-point & point-plane
CGAL/cgal-public-dev@201afecdcc6 Rigid registration with matching energy and rigid energy
CGAL/cgal-public-dev@5be11a69a9a Point to plane ICP example using Eigen's Jacobi SVD