Institutionen för systemteknik (ISY)

Improving RGB-D Scene Reconstruction using Rolling Shutter Rectification

H. Ovrén, P.-E. Forssén, D. Törnqvist
Springer Verlag
January 2015

Abstract

Scene reconstruction, i.e. the process of creating a 3D representation (mesh) of some real world scene, has recently become easier with the advent of cheap RGB-D sensors (e.g. the Microsoft Kinect).
Many such sensors use rolling shutter cameras, which produce geometrically distorted images when they are moving. To mitigate these rolling shutter distortions we propose a method that uses an attached gyroscope to rectify the depth scans. We also present a simple scheme to calibrate the relative pose and time synchronization between the gyro and a rolling shutter RGB-D sensor.
For scene reconstruction we use the Kinect Fusion algorithm to produce meshes. We create meshes from both raw and rectified depth scans, and these are then compared to a ground truth mesh. The types of motion we investigate are: pan, tilt and wobble (shaking) motions.
As our method relies on gyroscope readings, the amount of computations required is negligible compared to the cost of running Kinect Fusion.
This chapter is an extension of a paper at the IEEE Workshop on Robot Vision. Compared to that paper, we have improved the rectification to also correct for lens distortion, and use a coarse-to-fine search to find the time shift more quicky. We have extended our experiments to also investigate the effects of lens distortion, and to use more accurate ground truth. The experiments demonstrate that correction of rolling shutter effects yields a larger improvement of the 3D model than correction for lens distortion.

Full Paper

Chapter is availiable at SpringerLink.

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Bibtex entry

@InBook{oft15,
  author = 	 {H. Ovr\'en and P.-E. Forss\'en and D. T\"ornqvist},
  ALTeditor = 	 {},
  title = 	 {Improving RGB-D Scene Reconstruction using Rolling Shutter Rectification},
  chapter = 	 {Chapter in book: New Development in Robot Vision},
  publisher = 	 {Springer-Verlag Berlin Heidelberg},
  pages =        {55-71},
  volume =       {23},
  year = 	 {2015},
  month =        {January},
  note =         {VR Project: Learnable Camera Motion Models, 2014-5928},
  annote = 	 {http://dx.doi.org/10.1007/978-3-662-43859-6_4},
  isbn = 	 {978-3-662-43858-9}
}