Robust Camera Tracking by Combining Color and Depth Measurements

Erik Bylow, Carl Olsson, Fredrik Kahl

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

7 Citations (SciVal)


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.
Original languageEnglish
Title of host publication2014 22nd International Conference on Pattern Recognition (ICPR)
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication statusPublished - 2014
Event22nd International Conference on Pattern Recognition (ICPR 2014) - Stockholm, Sweden
Duration: 2014 Aug 242014 Aug 28
Conference number: 22

Publication series

ISSN (Print)1051-4651


Conference22nd International Conference on Pattern Recognition (ICPR 2014)

Subject classification (UKÄ)

  • Computer Vision and Robotics (Autonomous Systems)


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