Scale Invariant Robust Registration of 3D-Point Data and a Triangle Mesh by Global Optimization

摘要

A robust registration of 3D-point data and a triangle mesh of the corresponding 3D-structure is presented, where the acquired 3D-point data may be noisy, may include outliers and may have wrong scale. Furthermore, in this approach it is not required to have a good initial match so the 3D-point cloud and the according triangle mesh may be loosely positioned in space. An additional advantage is that no correspondences have to exist between the 3D-points and the triangle mesh. The problem is solved utilizing a robust cost function in combination with an evolutionary global optimizer as shown in synthetic and real data experiments.