Optimal, Non-Rigid Alignment for Feature-Preserving Mesh Denoising

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

We present a simple, yet effective method for non-rigid alignment of point clouds. Our focus lies on developing a practical approach that allows us to do efficient, multi-way alignment of millions of points such as those produced by structured light scanners. Starting from an initial displacement field over the combined point cloud, our solution relies on an iterative smoothing scheme on the neighborhood graph of each sub-scan, reducing the Dirichlet energy of the displacement field. We compare a number of schemes for computing the initial displacement field, ranging from estimating the Laplacian of the combined point clouds to more traditional measures such as the point to point distance or point to plane distance.
Original languageEnglish
Title of host publicationProceedings of the International Conference on 3D Vision
Number of pages9
Publication date2019
Publication statusPublished - 2019
Event2019 International Conference on 3D Vision - Québec City Convention Centre, Québec City, Canada
Duration: 16 Sep 201919 Sep 2019

Conference

Conference2019 International Conference on 3D Vision
LocationQuébec City Convention Centre
CountryCanada
CityQuébec City
Period16/09/201919/09/2019

Cite this

Gawrilowicz, F., & Bærentzen, J. A. (2019). Optimal, Non-Rigid Alignment for Feature-Preserving Mesh Denoising. In Proceedings of the International Conference on 3D Vision
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Gawrilowicz, F & Bærentzen, JA 2019, Optimal, Non-Rigid Alignment for Feature-Preserving Mesh Denoising. in Proceedings of the International Conference on 3D Vision. 2019 International Conference on 3D Vision, Québec City, Canada, 16/09/2019.

Optimal, Non-Rigid Alignment for Feature-Preserving Mesh Denoising. / Gawrilowicz, Florian; Bærentzen, Jakob Andreas.

Proceedings of the International Conference on 3D Vision. 2019.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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T1 - Optimal, Non-Rigid Alignment for Feature-Preserving Mesh Denoising

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AU - Bærentzen, Jakob Andreas

PY - 2019

Y1 - 2019

N2 - We present a simple, yet effective method for non-rigid alignment of point clouds. Our focus lies on developing a practical approach that allows us to do efficient, multi-way alignment of millions of points such as those produced by structured light scanners. Starting from an initial displacement field over the combined point cloud, our solution relies on an iterative smoothing scheme on the neighborhood graph of each sub-scan, reducing the Dirichlet energy of the displacement field. We compare a number of schemes for computing the initial displacement field, ranging from estimating the Laplacian of the combined point clouds to more traditional measures such as the point to point distance or point to plane distance.

AB - We present a simple, yet effective method for non-rigid alignment of point clouds. Our focus lies on developing a practical approach that allows us to do efficient, multi-way alignment of millions of points such as those produced by structured light scanners. Starting from an initial displacement field over the combined point cloud, our solution relies on an iterative smoothing scheme on the neighborhood graph of each sub-scan, reducing the Dirichlet energy of the displacement field. We compare a number of schemes for computing the initial displacement field, ranging from estimating the Laplacian of the combined point clouds to more traditional measures such as the point to point distance or point to plane distance.

M3 - Article in proceedings

BT - Proceedings of the International Conference on 3D Vision

ER -

Gawrilowicz F, Bærentzen JA. Optimal, Non-Rigid Alignment for Feature-Preserving Mesh Denoising. In Proceedings of the International Conference on 3D Vision. 2019