Nonrigid surface registration and completion from RGBD images

Weipeng Xu; Mathieu Salzmann; Yongtian Wang; Yue Liu

doi:10.1007/978-3-319-10605-2_5

Nonrigid surface registration and completion from RGBD images

Weipeng Xu, Mathieu Salzmann, Yongtian Wang, Yue Liu

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

5 Citations (Scopus)

Abstract

Nonrigid surface registration is a challenging problem that suffers from many ambiguities. Existing methods typically assume the availability of full volumetric data, or require a global model of the surface of interest. In this paper, we introduce an approach to nonrigid registration that performs on relatively low-quality RGBD images and does not assume prior knowledge of the global surface shape. To this end, we model the surface as a collection of patches, and infer the patch deformations by performing inference in a graphical model. Our representation lets us fill in the holes in the input depth maps, thus essentially achieving surface completion. Our experimental evaluation demonstrates the effectiveness of our approach on several sequences, as well as its robustness to missing data and occlusions.

Original language	English
Title of host publication	Computer Vision, ECCV 2014 - 13th European Conference, Proceedings
Publisher	Springer Verlag
Pages	64-79
Number of pages	16
Edition	PART 2
ISBN (Print)	9783319106045
DOIs	https://doi.org/10.1007/978-3-319-10605-2_5
Publication status	Published - 2014
Event	13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 6 Sept 2014 → 12 Sept 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8690 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th European Conference on Computer Vision, ECCV 2014
Country/Territory	Switzerland
City	Zurich
Period	6/09/14 → 12/09/14

Access to Document

10.1007/978-3-319-10605-2_5

Cite this

Xu, W., Salzmann, M., Wang, Y., & Liu, Y. (2014). Nonrigid surface registration and completion from RGBD images. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings (PART 2 ed., pp. 64-79). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8690 LNCS, No. PART 2). Springer Verlag. https://doi.org/10.1007/978-3-319-10605-2_5

Xu, Weipeng ; Salzmann, Mathieu ; Wang, Yongtian et al. / Nonrigid surface registration and completion from RGBD images. Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 2. ed. Springer Verlag, 2014. pp. 64-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Nonrigid surface registration and completion from RGBD images",

abstract = "Nonrigid surface registration is a challenging problem that suffers from many ambiguities. Existing methods typically assume the availability of full volumetric data, or require a global model of the surface of interest. In this paper, we introduce an approach to nonrigid registration that performs on relatively low-quality RGBD images and does not assume prior knowledge of the global surface shape. To this end, we model the surface as a collection of patches, and infer the patch deformations by performing inference in a graphical model. Our representation lets us fill in the holes in the input depth maps, thus essentially achieving surface completion. Our experimental evaluation demonstrates the effectiveness of our approach on several sequences, as well as its robustness to missing data and occlusions.",

keywords = "Nonrigid registration, RGBD images, surface completion",

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Xu, W, Salzmann, M, Wang, Y & Liu, Y 2014, Nonrigid surface registration and completion from RGBD images. in Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8690 LNCS, Springer Verlag, pp. 64-79, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 6/09/14. https://doi.org/10.1007/978-3-319-10605-2_5

Nonrigid surface registration and completion from RGBD images. / Xu, Weipeng; Salzmann, Mathieu; Wang, Yongtian et al.
Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 2. ed. Springer Verlag, 2014. p. 64-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8690 LNCS, No. PART 2).

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

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AB - Nonrigid surface registration is a challenging problem that suffers from many ambiguities. Existing methods typically assume the availability of full volumetric data, or require a global model of the surface of interest. In this paper, we introduce an approach to nonrigid registration that performs on relatively low-quality RGBD images and does not assume prior knowledge of the global surface shape. To this end, we model the surface as a collection of patches, and infer the patch deformations by performing inference in a graphical model. Our representation lets us fill in the holes in the input depth maps, thus essentially achieving surface completion. Our experimental evaluation demonstrates the effectiveness of our approach on several sequences, as well as its robustness to missing data and occlusions.

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Xu W, Salzmann M, Wang Y, Liu Y. Nonrigid surface registration and completion from RGBD images. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 2 ed. Springer Verlag. 2014. p. 64-79. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-319-10605-2_5

Nonrigid surface registration and completion from RGBD images

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