Citation
J. Balzer, M. Peters, S. Soatto. "Volumetric Reconstruction Applied to Perceptual Studies of Size and Weight". IEEE Workshop on Applications of Computer Vision (WACV), 2014.

Abstract
We explore the application of volumetric reconstruction from structured-light sensors in cognitive neuroscience, specifically in the quantification of the size-weight illusion, whereby humans tend to systematically perceive smaller objects as heavier. We investigate the performance of two commercial structured-light scanning systems in comparison to one we developed specifically for this application. Our method has two main distinct features: First, it only samples a sparse series of viewpoints, unlike other systems such as the Kinect Fusion. Second, instead of building a distance eld for the purpose of points-to-surface conversion directly, we pursue a first-order approach: the distance function is recovered from its gradient by a screened Poisson re- construction, which is very resilient to noise and yet preserves high-frequency signal components. Our experiments show that the quality of metric reconstruction from structured light sensors is subject to systematic biases, and highlights the factors that influence it. Our main performance index rates estimates of volume (a proxy of size), for which we review a well-known formula applicable to incomplete meshes. Our code and data will be made publicly available upon completion of the anonymous review process.

Electronic downloads

• http://arxiv.org/abs/1311.2642

• HTML

  J. Balzer, M. Peters, S. Soatto. <a
  href="http://robotics.eecs.berkeley.edu/pubs/15.html"
  >Volumetric Reconstruction Applied to Perceptual Studies
  of Size and Weight</a>, IEEE Workshop on Applications
  of Computer Vision (WACV), 2014.

• Plain text

  J. Balzer, M. Peters, S. Soatto. "Volumetric
  Reconstruction Applied to Perceptual Studies of Size and
  Weight". IEEE Workshop on Applications of Computer
  Vision (WACV), 2014.

• BibTeX

  @inproceedings{BalzerPetersSoatto14_VolumetricReconstructionAppliedToPerceptualStudiesOf,
      author = {J. Balzer and M. Peters and S. Soatto},
      title = {Volumetric Reconstruction Applied to Perceptual
                Studies of Size and Weight},
      booktitle = {IEEE Workshop on Applications of Computer Vision
                (WACV)},
      year = {2014},
      abstract = {We explore the application of volumetric
                reconstruction from structured-light sensors in
                cognitive neuroscience, specifically in the
                quantification of the size-weight illusion,
                whereby humans tend to systematically perceive
                smaller objects as heavier. We investigate the
                performance of two commercial structured-light
                scanning systems in comparison to one we developed
                specifically for this application. Our method has
                two main distinct features: First, it only samples
                a sparse series of viewpoints, unlike other
                systems such as the Kinect Fusion. Second, instead
                of building a distance eld for the purpose of
                points-to-surface conversion directly, we pursue a
                first-order approach: the distance function is
                recovered from its gradient by a screened Poisson
                re- construction, which is very resilient to noise
                and yet preserves high-frequency signal
                components. Our experiments show that the quality
                of metric reconstruction from structured light
                sensors is subject to systematic biases, and
                highlights the factors that influence it. Our main
                performance index rates estimates of volume (a
                proxy of size), for which we review a well-known
                formula applicable to incomplete meshes. Our code
                and data will be made publicly available upon
                completion of the anonymous review process.},
      URL = {http://robotics.eecs.berkeley.edu/pubs/15.html}
  }
  

Posted by Ehsan Elhamifar on 7 Jun 2014.
Groups: ehumans
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