Laplacian ICP for Progressive Registration of 3D Human Head Meshes

N. E. Pears; Hang Dai; William Alfred Peter Smith; Hao Sun

Laplacian ICP for Progressive Registration of 3D Human Head Meshes

N. E. Pears, Hang Dai, William Alfred Peter Smith, Hao Sun

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a `small deformation per iteration' assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance.

Original language	English
Title of host publication	International Conference on Automatic Face and Gesture Recognition 2023
Publisher	IEEE
Number of pages	8
Publication status	Published - 7 Jan 2023
Event	International Conference on Automatic Face and Gesture Recognition 2023 - Waikoloa Beach Marriott Resort, Waikoloa, Hawaii, United States Duration: 5 Jan 2023 → 8 Jan 2023 https://fg2023.ieee-biometrics.org/

Conference

Conference	International Conference on Automatic Face and Gesture Recognition 2023
Abbreviated title	FG-2023
Country/Territory	United States
City	Waikoloa, Hawaii
Period	5/01/23 → 8/01/23
Internet address	https://fg2023.ieee-biometrics.org/

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

Access to Document

FG2023_68_LICP_ACCEPTED_TYPOS_CORRECTEDAccepted author manuscript, 2.92 MB
FG2023_68_LICP_PUBLISHED
©2023 IEEE
Final published version, 2.92 MBLicence: CC BY

Cite this

@inproceedings{3d5f0ad55bc544bd8b64a450002bb76d,

title = "Laplacian ICP for Progressive Registration of 3D Human Head Meshes",

abstract = "We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a `small deformation per iteration' assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance.",

author = "Pears, {N. E.} and Hang Dai and Smith, {William Alfred Peter} and Hao Sun",

note = "This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details; International Conference on Automatic Face and Gesture Recognition 2023, FG-2023 ; Conference date: 05-01-2023 Through 08-01-2023",

year = "2023",

month = jan,

day = "7",

language = "English",

booktitle = "International Conference on Automatic Face and Gesture Recognition 2023",

publisher = "IEEE",

address = "United States",

url = "https://fg2023.ieee-biometrics.org/",

}

TY - GEN

T1 - Laplacian ICP for Progressive Registration of 3D Human Head Meshes

AU - Pears, N. E.

AU - Dai, Hang

AU - Smith, William Alfred Peter

AU - Sun, Hao

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

PY - 2023/1/7

Y1 - 2023/1/7

N2 - We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a `small deformation per iteration' assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance.

AB - We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a `small deformation per iteration' assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance.

M3 - Conference contribution

BT - International Conference on Automatic Face and Gesture Recognition 2023

PB - IEEE

T2 - International Conference on Automatic Face and Gesture Recognition 2023

Y2 - 5 January 2023 through 8 January 2023

ER -