Non-rigid 3D Shape Registration using an Adaptive Template

TL;DR

This paper introduces a fully-automatic non-rigid 3D shape registration framework that combines landmarking, adaptive templates, and an iterative ICP-CPD method to improve accuracy and convergence, achieving state-of-the-art results.

Contribution

It proposes a novel ICPD method integrating ICP and CPD with an adaptive template for better 3D shape registration.

Findings

Achieves superior registration accuracy compared to existing methods.

Demonstrates robustness across multiple datasets.

Provides faster convergence in non-rigid shape registration.

Abstract

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.