Non-rigid 3D Shape Registration using an Adaptive Template
Not on the map. Why?
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Mar 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 2.
4 papers.
- 2023Mesh Density Adaptation for Template-based Shape Reconstructioncited by 7
- 2023Laplacian ICP for Progressive Registration of 3D Human Head Meshescited by 2
- 2026High-Fidelity Single-Image Head Modeling with Industry-Grade Topologycited by 1
- 2026SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warpcited by 0
- 2026SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp31 Jul 2026
- 2026High-Fidelity Single-Image Head Modeling with Industry-Grade Topology6 May 2026
- 2023Mesh Density Adaptation for Template-based Shape Reconstruction30 Jul 2023
- 2023Laplacian ICP for Progressive Registration of 3D Human Head Meshes4 Feb 2023