Stereopsis is adaptive for the natural environment

Abstract

Humans and many animals have forward-facing eyes providing different views of the environment. Precise depth estimates can be derived from the resulting binocular disparities, but determining which parts of the two retinal images correspond to one another is computationally challenging. To aid the computation, the visual system focuses the search on a small range of disparities. We asked whether the disparities encountered in the natural environment match that range. We did this by simultaneously measuring binocular eye position and three-dimensional scene geometry during natural tasks. The natural distribution of disparities is indeed matched to the smaller range of correspondence search. Furthermore, the distribution explains the perception of some ambiguous stereograms. Finally, disparity preferences of macaque cortical neurons are consistent with the natural distribution.

Emergence of binocular disparity selectivity through Hebbian learning

“…We endow a neural network with a Hebbian STDP rule which reinforces synapses carrying causal spikes, and find that an unsupervised exposure of this network to natural stereoscopic stimuli leads to a converged population of neurones with binocular receptive fields which show single-cell and population characteristics close to those reported in electrophysiological studies 22,23 . Moreover, we show that our model also captures retinotopic biases in disparity statistics reported in various electrophysiological studies 24,25 .…”

“…Another statistical bias reported in retinal projections of natural scenes 24 is that horizontal disparities in the lower hemifield are more likely to be crossed (negative) while the upper hemifield is more likely to contain uncrossed (positive) disparities. This bias is not altogether .…”

“…http://dx.doi.org/10.1101/221283 doi: bioRxiv preprint first posted online Nov. 20, 2017; surprising considering that objects in the lower field of vision are likely to be closer to the observer than objects in the upper field 24,45 . A meta-analysis 24 of 820 V1 neurones from five separate electrophysiological studies indicates that this bias could also be reflected in the binocular neurones in the early visual system. Figure 4d shows the results of this meta-analysis with the probability density for the neurones in the lower hemifield (in red) showing a bias towards crossed disparities while the upper hemifield (in blue) shows a bias towards uncrossed disparities.…”

The threshold of binocularity: natural image statistics explain the reduction of visual acuity in peripheral vision

“…This arises 92 because long wavelength features are more likely to overlap in each view and thus merge 93 into a single binocular feature. Similarly, since the range of disparities is greater at 94 larger eccentricities than in the fovea [10,28], we would expect a lower proportion of 95 binocular components in these areas. Between these two points a clear and rapid 2D histogram of the proportion of binocular components across wavelength and eccentricity.…”

“…In both 68 cases, this reflects the redundancy present in natural binocular images. As eccentricity 69 increases, the distribution of binocular disparities increases [10,18,23,28] thus reducing 70 the similarity expected in a corresponding region of an image pair across the two eyes.…”