Surface Perception of Planar Abstractions

Abstract:

Various algorithms have been proposed to create planar abstractions of 3D models, but there has been no systematic effort to evaluate the effectiveness of such abstractions in terms of perception of the abstracted surfaces. In this work, we perform a large crowd-sourced study involving approximately 70k samples to evaluate how well users can orient gauges on planar abstractions of commonly occurring models.We test four styles of planar abstractions against ground truth surface representations, and analyze the data to discover a wide variety of correlations between task error and measurements relating to surface-specific properties such as curvature, local thickness and medial axis distance, and abstraction-specific properties. We use these discovered correlations to create linear models to predict error in surface understanding at a given point, for both surface representations and planar abstractions. Our predictive models reveal the geometric causes most responsible for error, and we demonstrate their potential use to build upon existing planar abstraction techniques in order to improve perception of the abstracted surface.

Acknowledgements:

We thank Vangelis Kalogerakis and W. John Braun for helpful correspondence, especially concerning our approach to regularization of model predictors. We thank Kristian Hildebrand and Dongming Yan for assisting us by providing some of the geometric representations. We thank Daniela Giorgi and AIM@SHAPE for providing meshes from the Watertight Track of SHREC 2007.

Bibtex:

@ARTICLE{planarStudy2013,
  title = {Surface Perception of Planar Abstractions},
  author = {James McCrae and Niloy J. Mitra and Karan Singh},
  journal = {ACM Transactions on Applied Perception},
  volume = {10},
  issue = {3},
  pages = {14:1--14:20},
  year = {2013}, 
}