Accurate non-rigid registration based on heuristic tree for registering point sets with large deformation
The conventional thin plate spline (TPS) and coherent point drift (CPD) methods are effective to yield non-rigid registration of point sets with similar appearance or structure, but it is a big challenge to register point sets with large deformation. To handle this case, by fully utilizing the heuristics derived from the distribution of entire population, this paper proposes a novel framework for non-rigid registration of point sets with large deformation via the heuristic tree matching. First, we use affine ICP with bidirectional distance to measure the shape similarity between two point sets. Then, the heuristic tree is built based on shape similarities, which connects two point sets with small deformation. In this way, the large deformation is divided into several small differences. Finally, the non-rigid registration is conducted progressively according to the tree. Experimental results demonstrate the proposed framework is valid for the alignment of point sets with large deformation and is more accurate compared with the traditional non-rigid registration approaches.
non-rigid registration heuristic tree large deformation shape similarity affine registration