Non-rigid 3D Shape Registration using an Adaptive Template
This work addresses the challenge of accurately aligning 3D shapes in computer vision and graphics, though it appears incremental as it builds upon existing methods like Coherent Point Drift.
The authors tackled the problem of non-rigid 3D shape registration by introducing a new framework called Iterative Coherent Point Drift (ICPD), which achieved state-of-the-art performance in accuracy and stability on three datasets.
We present a new fully-automatic non-rigid 3D shape registration (morphing) framework comprising (1) a new 3D landmarking and pose normalisation method; (2) an adaptive shape template method to accelerate the convergence of registration algorithms and achieve a better final shape correspondence and (3) a new iterative registration method that combines Iterative Closest Points with Coherent Point Drift (CPD) to achieve a more stable and accurate correspondence establishment than standard CPD. We call this new morphing approach Iterative Coherent Point Drift (ICPD). Our proposed framework is evaluated qualitatively and quantitatively on three datasets and compared with several other methods. The proposed framework is shown to give state-of-the-art performance.