Depth spreading through empty space induced by sparse disparity cues

Xintong Li, Abigail E. Huang, Eric L. Altschuler, Christopher W. Tyler

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A key goal of visual processing is to develop an understanding of the three-dimensional layout of the objects in our immediate vicinity from the variety of incomplete and noisy depth cues available to the eyes. Binocular disparity is one of the dominant depth cues, but it is often sparse, being definable only at the edges of uniform surface regions, and visually resolvable only where the edges have a horizontal disparity component. To understand the full 3D structure of visual objects, our visual system has to perform substantial surface interpolation across unstructured visual space. This interpolation process was studied in an eight-spoke depth spreading configuration corresponding to that used in the neon color spreading effect, which generates a strong percept of a sharp contour extending through empty space from the disparity edges within the spokes. Four hypotheses were developed for the form of the depth surface induced by disparity in the spokes defining an incomplete disk in depth: low-level local (isotropic) depth propagation, mid-level linear (anisotropic) depthcontour interpolation or extrapolation, and high-level (anisotropic) figural depth propagation of a disk figure in depth. Data for both perceived depth and position of the perceived contour clearly rejected the first three hypotheses and were consistent with the high-level figural hypothesis in both uniform disparity and slanted disk configurations. We conclude that depth spreading through empty visual space is an accurately quantifiable perceptual process that propagates depth contours anisotropically along their length and is governed by high-level figural properties of 3D object structure.

Original languageEnglish
Article number7
JournalJournal of Vision
Issue number10
StatePublished - 2013
Externally publishedYes


  • 3D surface and shape perception
  • Bayesian prior
  • Depth perception
  • Disparity
  • Filling-in
  • Propagation


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