The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 599–606, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-599-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 599–606, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-599-2016

  15 Jun 2016

15 Jun 2016

ENHANCEMENT OF STEREO IMAGERY BY ARTIFICIAL TEXTURE PROJECTION GENERATED USING A LIDAR

Joshua Veitch-Michaelis1, Jan-Peter Muller1, David Walton1, Jonathan Storey2, Michael Foster2, and Benjamin Crutchley2 Joshua Veitch-Michaelis et al.
  • 1Imaging Group, Mullard Space Science Laboratory, University College London, Holmbury St Mary, RH5 6NT, UK
  • 2Innovative Small Instruments Limited, Pipers Business Centre, 220, Vale Road, Tonbridge, Sussex, TN9 1SP, UK

Keywords: stereo, LIDAR, pattern projection, structured light, texture, Gotcha

Abstract. Passive stereo imaging is capable of producing dense 3D data, but image matching algorithms generally perform poorly on images with large regions of homogenous texture due to ambiguous match costs. Stereo systems can be augmented with an additional light source that can project some form of unique texture onto surfaces in the scene. Methods include structured light, laser projection through diffractive optical elements, data projectors and laser speckle. Pattern projection using lasers has the advantage of producing images with a high signal to noise ratio. We have investigated the use of a scanning visible-beam LIDAR to simultaneously provide enhanced texture within the scene and to provide additional opportunities for data fusion in unmatched regions. The use of a LIDAR rather than a laser alone allows us to generate highly accurate ground truth data sets by scanning the scene at high resolution. This is necessary for evaluating different pattern projection schemes. Results from LIDAR generated random dots are presented and compared to other texture projection techniques. Finally, we investigate the use of image texture analysis to intelligently project texture where it is required while exploiting the texture available in the ambient light image.