Hand waving away scale

Ham, Christopher, Lucey, Simon and Singh, Surya (2014). Hand waving away scale. In: David Fleet, Tomas Pajdla, Bernt Schiele and Tinne Tuytelaars, Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, (279-293). 6 - 12 September 2014. doi:10.1007/978-3-319-10593-2_19


Author Ham, Christopher
Lucey, Simon
Singh, Surya
Title of paper Hand waving away scale
Conference name 13th European Conference on Computer Vision, ECCV 2014
Conference location Zurich, Switzerland
Conference dates 6 - 12 September 2014
Proceedings title Computer Vision, ECCV 2014 - 13th European Conference, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Heidelberg, Germany
Publisher Springer Verlag
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1007/978-3-319-10593-2_19
Open Access Status
ISBN 9783319105925
9783319105932
ISSN 0302-9743
1611-3349
Editor David Fleet
Tomas Pajdla
Bernt Schiele
Tinne Tuytelaars
Volume 8692 LNCS
Issue PART 4
Start page 279
End page 293
Total pages 15
Chapter number 19
Total chapters 54
Collection year 2015
Language eng
Abstract/Summary This paper presents a novel solution to the metric reconstruction of objects using any smart device equipped with a camera and an inertial measurement unit (IMU). We propose a batch, vision centric approach which only uses the IMU to estimate the metric scale of a scene reconstructed by any algorithm with Structure from Motion like (SfM) output. IMUs have a rich history of being combined with monocular vision for robotic navigation and odometry applications. These IMUs require sophisticated and quite expensive hardware rigs to perform well. IMUs in smart devices, however, are chosen for enhancing interactivity - a task which is more forgiving to noise in the measurements. We anticipate, however, that the ubiquity of these "noisy" IMUs makes them increasingly useful in modern computer vision algorithms. Indeed, we show in this work how an IMU from a smart device can help a face tracker to measure pupil distance, and an SfM algorithm to measure the metric size of objects. We also identify motions that produce better results, and develop a heuristic for estimating, in real-time, when enough data has been collected for an accurate scale estimation.
Keyword 3D reconstruction
IMU
Metric
Smart devices
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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