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Robust Camera Tracking by Combining Color and Depth Measurements

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Author

Summary, in English

One of the major research areas in computer vision is scene reconstruction from image streams. The advent of RGB-D cameras, such as the Microsoft Kinect, has lead to new possibilities for performing accurate and dense 3D reconstruction. There are already well-working algorithms to acquire 3D models from depth sensors, both for large and small scale scenes. However, these methods often break down when the scene geometry is not so informative, for example, in the case of planar surfaces. Similarly, standard image-based methods fail for texture-less scenes. We combine both color and depth measurements from an RGB-D sensor to simultaneously reconstruct both the camera motion and the scene geometry in a robust manner. Experiments on real data show that we can accurately reconstruct large-scale 3D scenes despite many planar surfaces.

Publishing year

2014

Language

English

Pages

4038-4043

Publication/Series

2014 22nd International Conference on Pattern Recognition (ICPR)

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Computer Vision and Robotics (Autonomous Systems)

Conference name

22nd International Conference on Pattern Recognition (ICPR 2014)

Conference date

2014-08-24 - 2014-08-28

Conference place

Stockholm, Sweden

Status

Published

ISBN/ISSN/Other

  • ISSN: 1051-4651