By the same authors

Non-rigid 3D Shape Registration using an Adaptive Template

Research output: Contribution to conferencePaper

Standard

Non-rigid 3D Shape Registration using an Adaptive Template. / Dai, Hang; Pears, Nicholas Edwin; Smith, William Alfred Peter.

2018. Paper presented at ECCV 2018: PeopleCap Workshop, Munich, Germany.

Research output: Contribution to conferencePaper

Harvard

Dai, H, Pears, NE & Smith, WAP 2018, 'Non-rigid 3D Shape Registration using an Adaptive Template' Paper presented at ECCV 2018: PeopleCap Workshop, Munich, Germany, 14/09/18 - 14/09/18, .

APA

Dai, H., Pears, N. E., & Smith, W. A. P. (2018). Non-rigid 3D Shape Registration using an Adaptive Template. Paper presented at ECCV 2018: PeopleCap Workshop, Munich, Germany.

Vancouver

Dai H, Pears NE, Smith WAP. Non-rigid 3D Shape Registration using an Adaptive Template. 2018. Paper presented at ECCV 2018: PeopleCap Workshop, Munich, Germany.

Author

Dai, Hang ; Pears, Nicholas Edwin ; Smith, William Alfred Peter. / Non-rigid 3D Shape Registration using an Adaptive Template. Paper presented at ECCV 2018: PeopleCap Workshop, Munich, Germany.15 p.

Bibtex - Download

@conference{33149fbd00ee42c7b83c68f713d44042,
title = "Non-rigid 3D Shape Registration using an Adaptive Template",
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 improve 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 \emph{Iterative Coherent Point Drift} (ICPD). Our proposed framework is evaluated qualitatively and quantitatively on three datasets: Headspace, BU3D and a synthetic LSFM dataset, and is compared with several other methods. The proposed framework is shown to give state-of-the-art performance.",
keywords = "3D registration; 3D shape morphing; 3D morphable models",
author = "Hang Dai and Pears, {Nicholas Edwin} and Smith, {William Alfred Peter}",
year = "2018",
month = "9",
day = "14",
language = "English",
note = "ECCV 2018: PeopleCap Workshop ; Conference date: 14-09-2018 Through 14-09-2018",
url = "https://peoplecap2018.weebly.com/",

}

RIS (suitable for import to EndNote) - Download

TY - CONF

T1 - Non-rigid 3D Shape Registration using an Adaptive Template

AU - Dai, Hang

AU - Pears, Nicholas Edwin

AU - Smith, William Alfred Peter

PY - 2018/9/14

Y1 - 2018/9/14

N2 - 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 improve 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 \emph{Iterative Coherent Point Drift} (ICPD). Our proposed framework is evaluated qualitatively and quantitatively on three datasets: Headspace, BU3D and a synthetic LSFM dataset, and is compared with several other methods. The proposed framework is shown to give state-of-the-art performance.

AB - 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 improve 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 \emph{Iterative Coherent Point Drift} (ICPD). Our proposed framework is evaluated qualitatively and quantitatively on three datasets: Headspace, BU3D and a synthetic LSFM dataset, and is compared with several other methods. The proposed framework is shown to give state-of-the-art performance.

KW - 3D registration; 3D shape morphing; 3D morphable models

M3 - Paper

ER -