Huber- L<inf>1</inf> -based non-isometric surface registration

Authors: Jiang, T., Yang, X., Zhang, J., Tian, F., Liu, S., Xiang, N. and Qian, K.

Journal: Visual Computer

Volume: 35

Issue: 6-8

Pages: 935-948

ISSN: 0178-2789

DOI: 10.1007/s00371-019-01670-1

Abstract:

Non-isometric surface registration is an important task in computer graphics and computer vision. It, however, remains challenging to deal with noise from scanned data and distortion from transformation. In this paper, we propose a Huber-L1-based non-isometric surface registration and solve it by the alternating direction method of multipliers. With a Huber-L1-regularized model constrained on the transformation variation and position difference, our method is robust to noise and produces piecewise smooth results while still preserving fine details on the target. The introduced as-similar-as-possible energy is able to handle different size of shapes with little stretching distortion. Extensive experimental results have demonstrated that our method is more accurate and robust to noise in comparison with the state-of-the-arts.

http://eprints.bournemouth.ac.uk/32365/

Source: Scopus

Huber-L1-based non-isometric surface registration

Authors: Jiang, T., Yang, X., Zhang, J., Tian, F., Liu, S., Xiang, N. and Qian, K.

Journal: VISUAL COMPUTER

Volume: 35

Issue: 6-8

Pages: 935-948

eISSN: 1432-2315

ISSN: 0178-2789

DOI: 10.1007/s00371-019-01670-1

http://eprints.bournemouth.ac.uk/32365/

Source: Web of Science (Lite)

Huber- L1 -based non-isometric surface registration

Authors: Jiang, T., Yang, X., Zhang, J.J., Tian, F., Liu, S., Xiang, N. and Qian, K.

Journal: Visual Computer

Volume: 35

Issue: 6-8

Pages: 935-948

ISSN: 0178-2789

Abstract:

© 2019, The Author(s). Non-isometric surface registration is an important task in computer graphics and computer vision. It, however, remains challenging to deal with noise from scanned data and distortion from transformation. In this paper, we propose a Huber-L 1 -based non-isometric surface registration and solve it by the alternating direction method of multipliers. With a Huber-L 1 -regularized model constrained on the transformation variation and position difference, our method is robust to noise and produces piecewise smooth results while still preserving fine details on the target. The introduced as-similar-as-possible energy is able to handle different size of shapes with little stretching distortion. Extensive experimental results have demonstrated that our method is more accurate and robust to noise in comparison with the state-of-the-arts.

http://eprints.bournemouth.ac.uk/32365/

Source: BURO EPrints