September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Naturalistic Depth Perception
Author Affiliations
  • Brian McCann
    Center for Perceptual Systems, University of Texas at Austin Texas Advanced Computing Center, University of Texas at Austin
  • Mary Hayhoe
    Center for Perceptual Systems, University of Texas at Austin
  • Wilson Geisler
    Center for Perceptual Systems, University of Texas at Austin
Journal of Vision September 2015, Vol.15, 1100. doi:10.1167/15.12.1100
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      Brian McCann, Mary Hayhoe, Wilson Geisler; Naturalistic Depth Perception. Journal of Vision 2015;15(12):1100. doi: 10.1167/15.12.1100.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Making inferences about the 3-dimensional spatial structure of natural scenes is a critical visual function. While spatial discrimination both in depth and on the image plane has been well characterized for simple stimuli, little is known about our ability to discriminate depth in natural scenes, particularly at far distances. To begin filling in this gap we: (i) developed a database of 80 stereoscopic images paired with the corresponding measured distance information, (ii) used these scenes as psychophysical stimuli and measured near-far discrimination acuity in 4 observers as a function of distance and the visual angle separating the targets, (iii) made additional measurements under patched-eye (monocular) viewing conditions to evaluate the importance of binocular vision in depth discrimination as a function of viewing geometries. We find that binocular thresholds are roughly a constant Weber fraction of the distance for distances ranging from 4 to 28 meters. Further, measured thresholds were around 1% for small separations, and increased to 4% for stimuli separated by 10 deg. Thus, the ability to discriminate depth in natural scenes is very good out to considerable distances. To investigate the basis of this discrimination ability, monocular thresholds were measured. We found that monocular thresholds were elevated for distances less than 15 meters, but were comparable to binocular thresholds for greater distances. Accurate depth perception depends on combining (fusing) multiple sources of sensory information. Thus binocular thresholds probably involve fusing separate monocular and disparity-derived estimates. Under the assumption of Gaussian distributed independent estimates, Bayes rule provides a simple reliability-weighted summation model of cue combination. Using disparity threshold measurements by Blakemore (1970), and the current monocular thresholds, parameter-free predictions were generated for the current binocular thresholds. These predictions largely matched the data, suggesting that the disparity and monocular cues are separable and combined optimally in natural scenes.

Meeting abstract presented at VSS 2015

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