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Robust Camera Tracking by Combining Color and Depth Measurements

Bylow, Erik LU ; Olsson, Carl LU and Kahl, Fredrik LU (2014) 22nd International Conference on Pattern Recognition (ICPR 2014) In 2014 22nd International Conference on Pattern Recognition (ICPR) p.4038-4043
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2014 22nd International Conference on Pattern Recognition (ICPR)
pages
4038 - 4043
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
22nd International Conference on Pattern Recognition (ICPR 2014)
external identifiers
  • wos:000359818004029
  • scopus:84919904459
ISSN
1051-4651
DOI
10.1109/ICPR.2014.692
language
English
LU publication?
yes
id
3e6cac3a-befc-4dcd-831c-89d52bd3e275 (old id 7972405)
date added to LUP
2015-09-23 13:23:16
date last changed
2017-06-04 03:54:29
@inproceedings{3e6cac3a-befc-4dcd-831c-89d52bd3e275,
  abstract     = {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.},
  author       = {Bylow, Erik and Olsson, Carl and Kahl, Fredrik},
  booktitle    = {2014 22nd International Conference on Pattern Recognition (ICPR)},
  issn         = {1051-4651},
  language     = {eng},
  pages        = {4038--4043},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Robust Camera Tracking by Combining Color and Depth Measurements},
  url          = {http://dx.doi.org/10.1109/ICPR.2014.692},
  year         = {2014},
}