A Method for Global Non-rigid Registration of Multiple Thin Structures

We present a global algorithm for drift free alignment of multiple range scans of "thin" data into a single point cloud that is suitable for further processing, such as triangular meshing and volume calculation. We consider two sets of non-rigid data: synthetic vascular data and real Arabidopsis plant data. Our method builds on the coherent point drift algorithm, and aligns multiple point clouds into a single 3D point cloud. The plant data was acquired in a growth chamber, where the fan caused jittering in both the branch and leaf data. For each scan, we construct a target scan from the cancroids of its Mutual Nearest Neighbours (MNN) in all other scans and iteratively register to this, as opposed to registering pair wise scans sequentially. We have have adapted MNN for use in non-rigid scenarios, producing a method that will will not degrade as more scans are registered, and produces better results than sequential pair wise registration.