Pears, N. E. orcid.org/0000-0001-9513-5634, Dai, Hang, Smith, William Alfred Peter orcid.org/0000-0002-6047-0413 et al. (1 more author) (2023) Laplacian ICP for Progressive Registration of 3D Human Head Meshes. In: International Conference on Automatic Face and Gesture Recognition 2023. International Conference on Automatic Face and Gesture Recognition 2023, 05-08 Jan 2023 IEEE , USA
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.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | 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 |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 18 Nov 2022 12:20 |
Last Modified: | 24 Feb 2025 00:09 |
Status: | Published |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193520 |
Downloads
Filename: FG2023_68_LICP_ACCEPTED.pdf
Description: FG2023_68_LICP_ACCEPTED_TYPOS_CORRECTED