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Direct, Dense, and Deformable: Template-Based Non-Rigid 3D Reconstruction from RGB Video

Yu, R; Russell, C; Campbell, NDF; Agapito, L; (2015) Direct, Dense, and Deformable: Template-Based Non-Rigid 3D Reconstruction from RGB Video. In: 2015 IEEE International Conference on Computer Vision. (pp. pp. 918-926). IEEE Green open access

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Abstract

In this paper we tackle the problem of capturing the dense, detailed 3D geometry of generic, complex non-rigid meshes using a single RGB-only commodity video camera and a direct approach. While robust and even real-time solutions exist to this problem if the observed scene is static, for non-rigid dense shape capture current systems are typically restricted to the use of complex multi-camera rigs, take advantage of the additional depth channel available in RGB-D cameras, or deal with specific shapes such as faces or planar surfaces. In contrast, our method makes use of a single RGB video as input, it can capture the deformations of generic shapes, and the depth estimation is dense, per-pixel and direct. We first compute a dense 3D template of the shape of the object, using a short rigid sequence, and subsequently perform online reconstruction of the non-rigid mesh as it evolves over time. Our energy optimization approach minimizes a robust photometric cost that simultaneously estimates the temporal correspondences and 3D deformations with respect to the template mesh. In our experimental evaluation we show a range of qualitative results on novel datasets, we compare against an existing method that requires multi-frame optical flow, and perform a quantitative evaluation against other template-based approaches on a ground truth dataset.

Type: Proceedings paper
Title: Direct, Dense, and Deformable: Template-Based Non-Rigid 3D Reconstruction from RGB Video
Event: IEEE International Conference on Computer Vision
Location: Santiago, CHILE
Dates: 11 December 2015 - 18 December 2015
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICCV.2015.111
Publisher version: http://doi.org/10.1109/ICCV.2015.111
Language: English
Additional information: This ICVV paper is the open access version, provided by the Computer Vision Foundation.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10115313
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