# Non-rigid 3D Shape Registration using an Adaptive Template

arXiv: https://arxiv.org/abs/1803.07973 · HTML: https://mlsanity.com/paper/1803.07973/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Mesh Density Adaptation for Template-based Shape Reconstruction](https://mlsanity.com/paper/2307.16205/) (2023)
- [Laplacian ICP for Progressive Registration of 3D Human Head Meshes](https://mlsanity.com/paper/2302.02194/) (2023)
- [High-Fidelity Single-Image Head Modeling with Industry-Grade Topology](https://mlsanity.com/paper/2605.04524/) (2026)
- [SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp](https://mlsanity.com/paper/2608.00187/) (2026)

## Abstract (arXiv)

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.