1 research outputs found
Fast Coherent Point Drift
Nonrigid point set registration is widely applied in the tasks of computer
vision and pattern recognition. Coherent point drift (CPD) is a classical
method for nonrigid point set registration. However, to solve spatial
transformation functions, CPD has to compute inversion of a M*M matrix per
iteration with time complexity O(M3). By introducing a simple corresponding
constraint, we develop a fast implementation of CPD. The most advantage of our
method is to avoid matrix-inverse operation. Before the iteration begins, our
method requires to take eigenvalue decomposition of a M*M matrix once. After
iteration begins, our method only needs to update a diagonal matrix with linear
computational complexity, and perform matrix multiplication operation with time
complexity approximately O(M2) in each iteration. Besides, our method can be
further accelerated by the low-rank matrix approximation. Experimental results
in 3D point cloud data show that our method can significantly reduce
computation burden of the registration process, and keep comparable performance
with CPD on accuracy