Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/83155
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Type: | Conference paper |
Title: | STAR3D: Simultaneous tracking and reconstruction of 3D objects using RGB-D data |
Author: | Ren, C. Prisacariu, V. Murray, D. Reid, I. |
Citation: | Proceedings 2013 IEEE International Conference on Computer Vision, ICCV 2013, Sydney, NSW, Australia, 1-8 December 2013: pp.1561-1568 |
Publisher: | IEEE Computer Society |
Publisher Place: | USA |
Issue Date: | 2013 |
Series/Report no.: | IEEE International Conference on Computer Vision |
ISBN: | 9781479928392 |
ISSN: | 1550-5499 |
Conference Name: | International Conference on Computer Vision (2013 : Sydney) |
Statement of Responsibility: | Carl Yuheng Ren, Victor Prisacariu, David Murray and Ian Reid |
Abstract: | We introduce a probabilistic framework for simultaneous tracking and reconstruction of 3D rigid objects using an RGB-D camera. The tracking problem is handled using a bag-of-pixels representation and a back-projection scheme. Surface and background appearance models are learned online, leading to robust tracking in the presence of heavy occlusion and outliers. In both our tracking and reconstruction modules, the 3D object is implicitly embedded using a 3D level-set function. The framework is initialized with a simple shape primitive model (e.g. a sphere or a cube), and the real 3D object shape is tracked and reconstructed online. Unlike existing depth-based 3D reconstruction works, which either rely on calibrated/fixed camera set up or use the observed world map to track the depth camera, our framework can simultaneously track and reconstruct small moving objects. We use both qualitative and quantitative results to demonstrate the superior performance of both tracking and reconstruction of our method. |
Rights: | © 2013 Crown Copyright |
DOI: | 10.1109/ICCV.2013.197 |
Published version: | http://dx.doi.org/10.1109/iccv.2013.197 |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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