A combinatorial solution to non-rigid 3D shape-to-image matching
Bernard F, Schmidt FR, Thunberg J, Cremers D (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2017-January
Pages Range: 1436-1445
Conference Proceedings Title: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Event location: Honolulu, HI, USA
ISBN: 9781538604571
Abstract
We propose a combinatorial solution for the problem of non-rigidly matching a 3D shape to 3D image data. To this end, we model the shape as a triangular mesh and allow each triangle of this mesh to be rigidly transformed to achieve a suitable matching to the image. By penalising the distance and the relative rotation between neighbouring triangles our matching compromises between image and shape information. In this paper, we resolve two major challenges: Firstly, we address the resulting large and NP-hard combinatorial problem with a suitable graph-theoretic approach. Secondly, we propose an efficient discretisation of the unbounded 6-dimensional Lie group SE(3). To our knowledge this is the first combinatorial formulation for non-rigid 3D shape-to-image matching. In contrast to existing local (gradient descent) optimisation methods, we obtain solutions that do not require a good initialisation and that are within a bound of the optimal solution. We evaluate the proposed method on the two problems of non-rigid 3D shape-to-shape and non-rigid 3D shape-to-image registration and demonstrate that it provides promising results.
Involved external institutions
Luxembourg (LU)
Germany (DE)How to cite
APA:
Bernard, F., Schmidt, F.R., Thunberg, J., & Cremers, D. (2017). A combinatorial solution to non-rigid 3D shape-to-image matching. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (pp. 1436-1445). Honolulu, HI, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Bernard, Florian, et al. "A combinatorial solution to non-rigid 3D shape-to-image matching." Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA Institute of Electrical and Electronics Engineers Inc., 2017. 1436-1445.
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