Shading Beats Binocular Disparity in Depth from Luminance Gradients: Evidence against a Maximum Likelihood Principle for Cue Combination

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Perceived depth is conveyed by multiple cues, including binocular disparity and luminance shading. Depth perception from luminance shading information depends on the perceptual assumption for the incident light, which has been shown to default to a diffuse illumination assumption. We focus on the case of sinusoidally corrugated surfaces to ask how shading and disparity cues combine defined by the joint luminance gradients and intrinsic disparity modulation that would occur in viewing the physical corrugation of a uniform surface under diffuse illumination. Such surfaces were simulated with a sinusoidal luminance modulation (0.26 or 1.8 cy/deg, contrast 20%-80%) modulated either in-phase or in opposite phase with a sinusoidal disparity of the same corrugation frequency, with disparity amplitudes ranging from 0'-20'. The observers' task was to adjust the binocular disparity of a comparison random-dot stereogram surface to match the perceived depth of the joint luminance/disparity-modulated corrugation target. Regardless of target spatial frequency, the perceived target depth increased with the luminance contrast and depended on luminance phase but was largely unaffected by the luminance disparity modulation. These results validate the idea that human observers can use the diffuse illumination assumption to perceive depth from luminance gradients alone without making an assumption of light direction. For depth judgments with combined cues, the observers gave much greater weighting to the luminance shading than to the disparity modulation of the targets. The results were not well-fit by a Bayesian cue-combination model weighted in proportion to the variance of the measurements for each cue in isolation. Instead, they suggest that the visual system uses disjunctive mechanisms to process these two types of information rather than combining them according to their likelihood ratios.

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Citation: Chen C-C, Tyler CW (2015) Shading Beats
Binocular Disparity in Depth from Luminance
Gradients: Evidence against a Maximum Likelihood
Principle for Cue Combination. PLoS ONE 10(8):
e0132658. doi:10.1371/journal.pone.0132658

Editor: Samuel G. Solomon, University College
London, UNITED KINGDOM

Received: July 20, 2014

Accepted: June 18, 2015

Published: August 10, 2015

Copyright: © 2015 Chen, Tyler. This is an open
access article distributed under the terms of the
[Creative Commons Attribution License, which permits](http://creativecommons.org/licenses/by/4.0/)
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.

Data Availability Statement: All relevant data are
within the paper.

Funding: This work was funded by Minister of
Science and technology 102-2420-H-002-018-MY3 to
[CCC (http://www.most.gov.tw/). The funders had no](http://www.most.gov.tw/)
role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared
that no competing interests exist.

RESEARCH ARTICLE
# Shading Beats Binocular Disparity in Depth from Luminance Gradients: Evidence against a Maximum Likelihood Principle for Cue Combination

Chien-Chung Chen 1,2 *, Christopher William Tyler 3,4

1 Department of Psychology, National Taiwan University, Taipei, Taiwan, 2 Center for Neurobiology and
Cognitive Science, National Taiwan University, Taipei, Taiwan, 3 Smith-Kettlewell Eye Research Institute,
San Francisco, California, United States of America, 4 Division of Optometry and Visual Science, School of
Health Sciences, City University, London, United Kingdom

- c3chen@ntu.edu.tw

## Abstract

Perceived depth is conveyed by multiple cues, including binocular disparity and luminance

shading. Depth perception from luminance shading information depends on the perceptual

assumption for the incident light, which has been shown to default to a diffuse illumination

assumption. We focus on the case of sinusoidally corrugated surfaces to ask how shading

and disparity cues combine defined by the joint luminance gradients and intrinsic disparity

modulation that would occur in viewing the physical corrugation of a uniform surface under

diffuse illumination. Such surfaces were simulated with a sinusoidal luminance modulation

(0.26 or 1.8 cy/deg, contrast 20%-80%) modulated either in-phase or in opposite phase with

a sinusoidal disparity of the same corrugation frequency, with disparity amplitudes ranging
from 0’-20’. The observers’ task was to adjust the binocular disparity of a comparison ran
dom-dot stereogram surface to match the perceived depth of the joint luminance/disparity
modulated corrugation target. Regardless of target spatial frequency, the perceived target

depth increased with the luminance contrast and depended on luminance phase but was

largely unaffected by the luminance disparity modulation. These results validate the idea

that human observers can use the diffuse illumination assumption to perceive depth from

luminance gradients alone without making an assumption of light direction. For depth judg
ments with combined cues, the observers gave much greater weighting to the luminance

shading than to the disparity modulation of the targets. The results were not well-fit by a

Bayesian cue-combination model weighted in proportion to the variance of the measure
ments for each cue in isolation. Instead, they suggest that the visual system uses disjunc
tive mechanisms to process these two types of information rather than combining them

according to their likelihood ratios.

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Depth from Luminance Gradients

Introduction

When light reaches a surface, the shading pattern (or the luminance gradients) reflected from
that surface to the eyes is jointly determined by the incident angle of the light and the local
three-dimensional (3D) slant of the surface. Hence, the information regarding the 3D shape of
an object is embedded in the luminance gradient of the two-dimensional (2D) image of that
object [1,2,3]. The perception of shape-from-shading is thus an inverse problem for the recovery of the 3D shape of an object from the shading information in the 2D image.

However, if the direction of illumination is unknown, the solution to such a shape-fromshading problem is ambiguous or even indeterminate. As the luminance from a point on a
surface is jointly determined by the direction of illumination and the surface slant, different
combinations of illumination direction and surface slant can produce the same luminance distribution perceived by an observer [4,5,6]. For instance, a convex hemisphere illuminated from
one direction has the same appearance as a concave hemisphere illuminated from the opposite
direction [7,8,9].

One approach to the shape-from-shading problem is to find illumination-invariant properties in a scene [10,11]. For instance, Koenderink & van Doorn [12] suggested that shape-fromshading is based on the analysis of global luminance distributions such as the position of singularities of luminance and of the equiluminance contour. There are also studies on the illumination-invariant properties in the visual system that may help to determine shape from shading

[13–17]. One example of this approach is demonstrated in hollow face illusion [14,15,18] in
which the face is always perceived as convex regardless of the illumination even for the hollow
mask of a face, suggesting that human observers employ a convex face assumption. However
human observers in general do not show shape constancy under diverse illumination conditions [9, 19–22]. In addition, despite the strong demonstration of the hollow face illusion,
there is evidence that face perception is strongly influenced by illumination direction [23]. The
implication of these studies is that an illumination-invariant property of the image or the visual
system cannot play a major role in shape-from-shading in general.

Another approach to the shape-from-shading problem is to find out how the visual system
estimates the illumination direction and solves the inverse problem. An early approach by
Pentland [4] proposed a model that assumed an isotropic distribution of surface orientations
and that any bias in the luminance distribution of the image signals the illumination direction.
This model requires the assumption of a unique light source from a definite direction. A very
influential theory was put forward by Ramachandran [9,19] utilizing an observation by Brewster [7,24] and discussed by Gregory [8] to show that the human visual system resolves the
shape-from-shading problem by making two assumptions about the illumination: 1) that there
is a single light source illuminating the whole scene, and 2) that the light is shining from above
(more precisely, with a slight top-left lighting direction [25,26]. With these two constraints, the
visual system is often able to recover the 3D shape of the surface, in which a top-to-bottom
luminance gradient from bright to dark suggests a convex surface while one from dark to bright
suggests a concave surface [19].

The assumption of single light source, however, is often not tenable in the environment in
general. Due to scattering, any scene containing the sky or a matt surface would have a diffused
light component. Under diffuse illumination, such as in a cloudy day, the illumination comes
from every direction and there is no single illumination direction. Hence, a model that assumes
single-direction light sources cannot be applied to the case of shape perception under diffuse
illumination.

Under diffuse illumination, the incident light from every direction has equal intensity.
When from a diffuse source, the illumination at a point on a surface is the integral of all

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Depth from Luminance Gradients

incident light at that point. Since the diffuse light intensity is uniform from all directions, the
illumination at any point is a generalized cone of rays reaching that point through an aperture
formed by the rest of the surface [27]. A point in a “valley”, which is by definition surrounded
by neighboring “hills” has a reduced aperture for incident light, would receive less illumination
and thus generally appear darker than a point on a “hill”, which is open to all incident light
and thus appears brighter. Hence, the reflected light from a point on a Lambertian surface is
approximately inversely related to the depth of that point and much of the shape of the surface
can be recovered with the use of this “dark-is-deep” rule [6,28–31]. This analysis is different
in principle from that under a single directional light source, in which the luminance is determined by the orientation of the surface related to the light source [1,2,32]. Langer & Bülthoff

[6] showed that the observer can indeed discriminate between “hills” and “valleys” on a surface
with this “dark-is-deep” rule. Schofield et al. [30] suggested that the shape-from-shading for a
directional light source and for a diffuse light source are processed by parallel modules in the
visual system.

Under normal viewing conditions, shading is not the only source of information for estimating the shape of a surface. A binocular observer can also assess the depth of a surface point
through the binocular disparity of the shading information reflected from the surface. In particular, if a binocular observer views the physical corrugation of a uniform (Lambertian) surface under diffuse illumination, that surface will have intrinsic binocular disparity shifts of the
peaks relative to the troughs, in addition to their corresponding luminance modulation from
bright to dark. However, binocular disparity is based on the difference of luminance distributions between the left and right eye images, rather than on the luminance distribution itself

[33,34]. Thus, it follows a different computational principle from the reconstruction of shapefrom-shading in a single-image view.

In the present study, we are interested in how the visual system combines these two very different computational processes in estimating the shape of a surface. Previous work has shown
an interaction between the visual processes for binocular disparity and shading from directional light source [35–39]. It is also reported that the presence of a diffusely illuminated
corrugated surface can affect stereoscopic threshold [29]. Moreover, Likova & Tyler [40] investigated the perceived position of targets jointly determined by disparity and diffuse illumination cues. However, it is not known how the perceived depth is determined by binocular
disparity and shading formed by diffuse illumination, which operates on a very different logic
from that for shading from a focal light source.

We investigated this issue by measuring the perceived depth of sinusoidal luminance gratings with a disparity modulation appropriate to their generation by diffuse illumination of a
corrugated sinusoidal surface. Notice that we did not attempt to capture the precise luminance
profile of a physical sinusoidal surface illuminated with diffuse illumination and its own self
reflections because of the complexity of such image generation and because human observers
are generally insensitive to the second order subtleties of such luminance gradients. Instead we
used a sinusoidal luminance profile combined with the corresponding sinusoidal disparity profile. Indeed, none of the participants were aware of any deviation from a sinusoidal depth profile for these stimuli under any conditions. Given the dark-is-deep rule, the part of the grating
with lower luminance should be perceived as further away from an observer than the part with
higher luminance [28]. The sinusoidal luminance modulation was itself modulated differentially between the two eyes to provide a disparity cue to the same corrugation profile, but for a
range of different positive and negative disparity amplitudes (see Fig 1). For example, the luminance peaks were shifted to the left in one eye while the luminance troughs were shifted to the
right, with corresponding differences in the luminance gradients between them, to provide the
disparity cue that would be obtained from viewing a physically corrugated surface that was

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Depth from Luminance Gradients

Fig 1. A. An example of the stimuli. Observers matched the perceived depth of the random-dot disparity comparison to that of the luminance+disparity
defined depth of the target. B. Example of left-eye disparity shifts in the single raised cosine targets. C. Example of left-eye disparity shifts in the cosine wave
targets.

doi:10.1371/journal.pone.0132658.g001

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Depth from Luminance Gradients

diffusely illuminated. By observing the change of perceived depth with different combinations
of luminance contrast and disparity modulation, we can establish how the visual system integrates these two types of information in determining the shape of such a surface.

Methods

Apparatus

The sinusoidal corrugation stimuli were presented on the 21-inch CRT monitor with 1920 (H)
×1440 (V) spatial resolution and 75 Hz refresh rate controlled by a Quad-Core MacPro computer. The viewing field for each eye (half of the display width) was 9.9° (H) by 14.6° (V).
Observers viewed the monitor at a distance of 112 cm, at which one pixel on the screen subtended 0.01°×0.01°. The left and the right sides of the screen were masked by a black divider to
avoid cross-talk. Observers viewed stimuli through a four-mirror stereoscope in a dark, quiet
room. The monitor input-output intensity function was calibrated to full linearity (within 0.1%
at all 256 luminance levels) with a LightMouse photometer [41]. The observer’s head was supported by a chin rest. The experimental control and the stimulus generation were written in
MATLAB with the Psychophysics Toolbox [42].

Stimuli

Fig 1 shows an example of the stimuli. Both the left and the right images contained a fixation
point (0.05° × 0.05°) at the center of the display, a black rectangular surround (2.2° (H) × 1.75°
(V) with line width 0.03°) to help establish binocular fusion, a target 1.8° (H) × 0.45°(V) whose
center was 0.32° above the fixation, and a matchable stimulus of the same size whose center
was 0.32° below the fixation

The simulated surface in our stimuli had a sinusoidal luminance profile:

LðxÞ ¼ L0 þ C                                  - cosð2pfxÞ

where L(x) was the luminance at point x; L0 was the mean luminance of the display 27.6 cd/m 2 ;
C was the contrast; and f was the spatial frequency of the luminance profile. The luminance
contrast C was ±20% and ±80%. The positive contrast was defined as having the luminance at
the fixation position closer than average and the negative, farther. The spatial frequency was
either 1.8 cy/deg to simulate a corrugated cosine wave surface or 0.26 cy/deg to simulate a single
raised-cosine bulge (C > 0) or a dip (C <0).

The disparity modulation of the target also had a sinusoidal profile:

dðxÞ ¼ dc                                   - cosð2pfxÞ

where d(x) was the disparity at point x and dc was the maximum disparity in the stimulus. The
spatial frequency of disparity modulation was the same as that for the luminance modulation.
The disparity dc ranged from -20 to 20 arcmin in this study. Positive dc meant the surface
pointed toward the observer and the negative dc, away from the observer, with the sign assignment of the disparity consistent with that of luminance contrast.

The match stimuli were random-dot stereograms with a uniform dot distribution. Each dot
was a 0.02° × 0.02° square. Tyler [28] showed that, due to the compensating nonlinearities of
the luminance relative to the z-axis height of a 3D surface under diffuse illumination and the
compressive nonlinearity of human luminance perception, the perceived height of points on a
corrugated surface under diffuse illumination is well-approximated by a linear proportionality
to its luminance. Hence, the matching stimulus was given the same sinusoidal spatial profile as
the target disparity modulation except for its amplitude, which was the adjustment parameter.

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Procedures

Throughout the experiment, the fixation and the surrounding squares were present on the
screen to help the observer to maintain fusion of the left and right eye images. On each trial,
both the target and the match stimuli appeared 100 ms after a tone. The observer then pressed
one of the two keys on a keypad to adjust the disparity of the matching stimulus until the perceived depth of the match and the target appeared equal, with both the target and the match
remaining on screen throughout the trial. When satisfied with the match, the observer then
pressed another key to finish the trial. The luminance contrast and disparity of the target and
the initial disparity of the match were randomized for each trial.

Participants

Five observers participated in the experiment. All were naïve to the purpose of this study and
had normal or corrected-to-normal vision acuity (20/20). Before the experiment, observers
were shown some of the random-dot match stimuli used in the experiment to verify that they
were able to identify the 3D shape implied by the stimuli. All observers provided written consent and were financially compensated for their time. The use of human observers was
approved by the Research Ethics Committee of the National Taiwan University Hospital
(201210026RIC) and followed the guidelines specified in the Declaration of Helsinki.

Results

The first experiment was to match the perceived depth of the combined luminance and disparity targets as a function of test disparity. Fig 2 shows the matched disparity as a function of test
disparity of the raised cosine stimuli (0.26 cy/deg test spatial frequency) at four test luminance
contrasts for two representative observers, and the average across five observers. Fig 3 shows
the same result for the 1.8 cy/deg cosine wave conditions. For individual data, each data point is
the mean of at least four measurements. The error bars represent one standard error of the
means. The horizontal dashed lines denote the matched disparity for the case of zero test disparity for the corresponding test luminance contrast. Since these matches were acquired without a disparity between the left and right eye images, the observer could only rely on their
impression of depth from the luminance gradient in the image for the judgment. Hence, they
represent the level of perceived depth from luminance contrast alone. The diagonal dash-dot
lines show the predictions for depth matches by target disparity only. Thus, if the perceived
depth of the target was determined by the disparity modulation alone, the matches made by
the observers should all fall on this diagonal line.

For all conditions, the matched disparity was roughly invariant for each luminance contrast
regardless of the magnitude and the sign of the test disparity, implying that the perceived depth
modulation was determined by luminance cues alone. As a result, all the matched functions lie
near the horizontal dashed lines defined by the match for the zero test disparity condition. To
quantify this behavior, we fit a linear function with slope as a free parameter to each of the 40
test disparity functions (4 luminance contrasts × 2 spatial frequency × 5 observers). Only 7 out
of 40 functions had slopes that were significantly different from 0 at the p < 0.05 level and
none was significant when we applied Bonferroni correction to the statistical tests to control
for multiple applications. Notice that, our result was that the matched disparity did not depend
on disparity modulation in the test stimuli. It was not that the observer did not have a sense of
depth from disparity. As the readers can see for themselves from binocular fusion of the stimulus examples in Fig 1, these stimuli do give a sense of disparity-induced depth. It is just that the
perceived depth did not change with test disparity.

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Depth from Luminance Gradients

Fig 2. Matched depth as a function of the test disparity for luminance gradients in two representative
observers (A) and (B), and the average across five observers (C) in the single raised cosine (0.26 cy/
deg test spatial frequency) condition. Horizontal dashed lines denote the matched disparity for zero test
disparity at the corresponding test luminance contrast. The diagonal dash-dot lines show the predictions for
depth matches as a function of target disparity only. The error bars represent one standard error of the
means.

doi:10.1371/journal.pone.0132658.g002

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Fig 3. Matched depth as a function of the test disparity for luminance gradients in two representative
observers (A) and (B), and the average across five observers (C) in the 1.8 cy/deg cosine wave
condition. Plotting conventions are the same as Fig 2.

doi:10.1371/journal.pone.0132658.g003

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On the other hand, observers had no problem perceiving depth from the luminance modulation (again as demonstrated in Fig 1). Luminance increases were always perceived as convex
(positive disparity match) and decreases as concave (negative disparity match). This is effect is
most pronounced in observer CPY (Panel (A) in Figs 2 and 3). Even observers CCY (Panel (B)
in Figs 2 and 3), who showed the weakest luminance contrast effect among all our observers,
had a clear effect of contrast polarity. The data of all other observers and the mean of all five
observers (Panel (C) in Figs 2 and 3) fall between the two extremes. These effects were confirmed by a three-factor (luminance contrast × test disparity × spatial frequency), nested (test
disparity was nested to spatial frequency), repeated measures ANOVA, which showed a significant effect for luminance contrast (F(3,220) = 129.62, p < .0001) but not for test disparity (F
(12,220) = 1.55, p = .11). Such result is consistent with the “dark-is-deep” rule for shape from
shading in diffuse illumination [6,26,28–31] in the case of the cosine wave modulation. For the
single raised cosine the interpretation is not so clear, as developed in the Discussion.

To further illustrate the substantial effect of depth from luminance alone, Fig 4(A) shows
the matched disparity as a function of luminance contrast, averaged across observers for the
zero disparity conditions. On average, matched disparity was close to a linear function of log
luminance contrast (over a factor of 4 in positive and negative luminance contrasts) and was
best fit by the function

Y ¼                          - a þ b X

where X = sign(contrast)×log10(|contrast|), with a = -0.01 arcmin and b = 1.30 arcmin/log10
unit which explains 98% of the variance in the averaged data at zero test disparity.

On the other hand, the change of matched disparity caused by the test disparity alone (Fig
4B) was remarkably flat ((slope b = 0.036 arcmin/log10 unit), implying that perceived depth for
the disparity of the luminance gradients is less than 4% of that from the random-dot matching
disparity modulation. Thus, the perceived depth of the dual-cue sinusoids was strongly dominated by the luminance modulation per se and received surprisingly little contribution from
their disparity modulation.

Phenomenology

The reader can observe such luminance dominance effect with illustration shown in Fig 5. In
each image of the top row of Fig 5 (Panels (A)-(C)), the reader can readily perceive a convex
shape with the center bulge toward the viewer. If one fuses Panels (A) and (B), the disparity
between the two images gives a percept of stereo-depth. Panel (C) is exactly the same as Panel
(A) but placed to the left of Panel (B) instead of to the right. Hence, the disparity between Panels (B) and (C) has the opposite polarity from that between Panels (A) and (B), so it should be
seen as a concave shape bulging away from the viewer. However, the reader can verify that
both pairings are always perceived as a convex bulges regardless of which pair of images is
fused.

The middle row of Fig 5 (Panels (D)-(F)) is the same as the upper row except the luminance
profile has the dark at the center. Thus, the viewer perceives a concave shape in both pairings,
despite the opposite disparity signals in the two cases. Again, the disparity information plays
little role in perceived shape, although a small difference may be perceived in the convexity in
the upper row and the concavity in the middle row that is attributable to the disparity cue adding weakly to the luminance cue to perceived depth.

The bottom row of Fig 5 (Panels (G)-(I)) illustrates how the depth should be perceived from
the disparity cue if it were fully processed. Panels (G)-(I) replicate the images of Panels (A)-(C)
but overlaid with random dots having the same disparity information as the luminance shifts.

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Depth from Luminance Gradients

Fig 4. (A) Matched random-dot disparity as a function of luminance contrast at the zero-disparity conditions,
averaged across observers. (B) Matched random-dot disparity as a function of test disparity for the luminance
gradients, normalized by the perceived depth in the zero-disparity condition, averaged across luminance
contrasts and observers. Solid circles are the raised cosine condition and open circles, the 1.8 cy/deg
condition. Error bars represents one standard error of the means across observers.

doi:10.1371/journal.pone.0132658.g004

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Fig 5. Demonstration of the luminance depth effect. (A) and (B) have the same positive raised cosine luminance and disparity modulation as the left and
the right eye images of our stimuli (see Method). (C) is a copy of (A). Hence, binocular fusion of (A) and (B) should give a image that has an opposite disparity
from fusion of (B) and (C). Yet, observers perceive a convex shape in both types of fusing. (D)-(F) are the same as (A)-(C) except that the luminance profile
has the opposite polarity. Now the depth is perceived as concave in both cases, regardless of the direction of the disparity information. In Panels (G)-(H), the
images of Panels (A)-(C) are replicated but random dots are added with the same disparity information as the luminance shifts, making the disparity-induced
depth sufficiently strong to overcome the depth from the luminance cue that dominates in Panels (A)-(C).

doi:10.1371/journal.pone.0132658.g005

This modification demonstrates that the random-dot overlay can make the disparity-induced
depth sufficiently strong to overcome the depth from the luminance cue that dominates in Panels (A)-(C). However, the monocular cue of texture uniformity in the random dots provides a
cue to flatness that does tend to reduce the maximum depth impression relative to that for the
luminance cue alone.

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Discussion

In this study, we show that observers can perceive measurable depth from luminance gradients,
with the brighter regions appearing closer to the observer than dark regions. Since our stimuli
have the same luminance profile as a surface under diffuse illumination under the Lambertian
reflectance assumption, this result confirms that observers can recover shape from shading
under diffuse illumination on the basis of the “dark-is-deep” rule [6,28–31]. In addition, we
confirm that the perceived depth increases in proportion to the logarithm of luminance contrast, although this was not a strong test because the range was only a factor of 4.

The interpretation of the “dark-is-deep” rule in terms of shape from shading under diffuse
illumination (and assuming Lambertian reflectance) is strictly accurate only for the cosine
wave, in the sense that a physical surface has the aperture to the ‘sky’ (or diffuse illumination
source) for each point on the surface that varies appropriately to give a monotonic relationship
between the surface intensity profile and the physical depth [28]. (Note that, under the usual
shape-from-shading rule for a point source, both the peaks and troughs of a sinusoidal corrugation would appear brighter than the flanks when the source was normal to the mean surface,
and hence the luminance modulation would have double the spatial frequency of the depth
modulation. Conversely, if the point source were at a grazing angle to the flanks, the depth
peaks would appear to be shifted to the halfway points between the luminance peaks and
troughs. Neither of these point-source interpretations were perceived by the observers, who all
reported the light peaks as closer, consistent with the diffuse illumination assumption.)

The similar result, of the light peak appearing the closest for the single raised cosine, can be
seen as a generalization of the same perceptual interpretative principle, even though there is no
illumination source (diffuse or local) that can generate a cosine intensity profile from a raised
cosine surface. For symmetrical illumination, the far tails of the raised cosine should have the
same intensity as the central peak under the Lambertian reflectance assumption, while the
slanted flanks should appear dimmer. The fact that the human visual system shows essentially
the same results for the two configurations (see Fig 4) implies that it is applying the shortcut
heuristic suggested by Tyler [28] to determine the perceived depth, even though it is not
entirely appropriate in the case of the single cosine target.

Remarkably, the binocular disparity cue in our test stimuli had little effect on the perceived
depth. At the first glance, this result is inconsistent with the previous studies showing that the
perceived depth of a surface is jointly determined by the disparity and shading cues [36,39]or
that the sensitivity to the 3D shape variation of a surface from shading cue can be improved by
the presence of stereoscopic cues [35,38], or that the perceived position of a surface is more
strongly determined by disparity than by luminance cues to the shape [40]. There are three
possible reasons underlying this inconsistency. First, all previous studies of shading cues compared the shading cues with disparity cues from added, local-edge-defined disparities rather
than with disparity cues defined by the shading information, per se. In our study, the only disparity cue available is that provided by the intrinsic disparities of the shading gradients produced by the lighting of smooth surfaces. Such intrinsic ‘shading’ disparities are of the same
(low) spatial frequency content as the surface gradients, which may not be optimal for the disparity processing system [43], and hence may not provide a reliable depth cue.

Second, all those studies showing an interaction with disparity and shading cues used shading under directional illumination. The computation of shape-from-shading under directional
and diffuse illumination is quite different, and may even be processed by different mechanisms
in the visual system [31]. Hence, the interaction between stereoscopic and shading cues may be
different under directional and diffuse illumination. Third, it has been suggested that binocular
disparity plays a secondary role in perceived 3D shape if there is a strong 2D shape cue, such as

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Depth from Luminance Gradients

contour symmetry, in the stimuli [44]. For example, Norman et al. [45] reported that adding
binocular disparity to the stimuli does not help shape discrimination threshold if there are
enough monocular cues in the stimuli. That is, if there are strong monocular depth cues available, observers tend to make shape judgments based on the monocular cues to depth and
ignore disparity information. Our stimuli were designed to simulate the binocular view of
smooth surfaces under diffuse illumination. Given that diffuse illumination is the default
assumption for illumination used by human observers [28], it is likely that the shading cues in
our stimuli were much stronger than the binocular cues.

Maximum Likelihood Analysis

The latter explanation of the lack of the disparity effect may be further analyzed in the context
of Maximum Likelihood Estimation theory (MLE) for depth cue combination [46]. MLE theory suggests that the depth estimated from combined cues is a weighted average of the depths
for the individual cues and that the contribution of each individual cue to the depth computation is proportional to the reliability of that cue (defined as the inverse of the variance of depth
estimation). Formally, therefore, the perceived depth dp is given as

dp ¼ wLdL þ wDdD

where

wL ¼ sD 2

D 2 =ðs2 D

2 D þ s L 2

L 2Þ and w D ¼ s L 2

L 2=ðs2 L

2 L þ s D 2

D 2 Þ

where dL and dD are the perceived depth induced by luminance and disparity cue, respectively,
the weighting factors wL and wD are determined by the variances for the luminance cue, s 2 L ,

the weighting factors wL and wD are determined by the variances for the luminance cue, s 2 L ,

and disparity cue, s 2 D , respectively. Hence, the estimated depth of the combined cue should be a

linear combination of the estimated depths of individual cues (assuming constant additive
noise sources). In extreme cases where one cue is very reliable while the other provides no reliable depth information, the MLE predicts that the weighting effectively devolves to the reliable
cue.

Qualitatively, the fact that the measured disparity functions are almost flat over most of the
range (Fig 2) implies that our results are near the extreme case in which the observers maximized the luminance cue and heavily discounted the disparity cue. Hence, under MLE, they
would imply either that the visual mechanisms for disparity are extremely noisy or that they
contribute little to the perceived depth, neither of which seems consistent with the previous literature implying a much more accurate contribution to shape from disparity than from luminance information (e.g., Likova & Tyler,[40]).

For a quantitative test of the MLE theory, we used the matched disparity at the zero test disparity conditions (i.e., the central data points in each panel of Fig 2 for a given observer) as an
estimation of the perceived depth induced by the luminance cue alone, dL, and the measured
variance for the zero disparity conditions (averaged across different luminance contrast conditions for a given observer) as an estimation of the variance s 2 L for that observer. The variance of

the non-zero test disparity conditions, s 2 T , estimated by the measured variance averaged across

the data for all non-zero test disparities, should contain contributions from both luminance
and disparity. Hence, assuming that the noise source for the luminance and the disparity cues
are independent, the variance for the disparity cue is computed as

s 2 T

2 T ¼ s2 D

2 D þ s L 2

We understand that there is an alternative approach to estimate the variance of perceived
depth with a separate just-noticeable difference (JND) measurement [47]. However, the

PLOS ONE | DOI:10 1371/journal pone 0132658 August 10 2015 13 / 17

Depth from Luminance Gradients

variance in matching tasks we used is not only a more direct estimate but also has been used to
estimate the JND in some classic psychophysics studies (e.g., MacAdam, [48]) and thus should
provide a good estimation of the variance of perceived depth from different cues.

There was no independent measure of perceived depth from disparity alone in our data
(since the disparities were, by definition, specified by the luminance gradients). However, since
the range of our test disparities was relatively small (up to only about 1/10 th of the full range
for depth from disparity), we can assume that the perceived depth is a linear function of test
disparity, that is, dD = adt, where dt is the test disparity in the experiment and a is the disparity
scaling factor. For the noise cues, we assumed that the noise was additive and thus was at the
same level for all conditions, and that the observers was limited by noise from both sources in
all conditions and thus the weighting WL and WD were the same for all conditions. The MLE
model for the perceived depth from shading gradients may thus be implemented with only a as
a free parameter. This approach was compared with a no-free-parameter model of the depth
for the non-zero disparity conditions being determined by the perceived depth from the luminance-only condition.

Fig 6 shows an example of the fit of the 1-parameter MLE model to the data of Figs 2C and
3C. As this fit shows, in general the MLE model underestimates the effect of the luminance cue,
in that the fitted curves are somewhat closer to the zero depth than the data points. The scale
parameter required to fit the data, averaged across all observers, was about 0.26. That is, the
variation in perceived depth according to the matched disparity was only a quarter of that of
the physical disparity of the luminance gradients! The SSE of the MLE fit, with one free parameter for each observer and test spatial frequency, was about twice that of the fit of a horizontal
line at the mean level for each luminance contrast and observer (zero free parameter fit).
Hence, it is clear that the MLE theory of cue combination provides only a poor fit to the data.

The failure of the MLE theory to fit our data is inconsistent with the result of Lovell et al.

[39] who showed that the MLE theory could account for the perceived depth provided by the
combined disparity and point-source shape-from-shading cues. However, in their experiments,
the luminance gradient for shading cues carried no disparity information. Instead, the disparity
cue was provided by random dots whose luminance distribution was inconsistent with the
shading cues. Thus, the shading cue was likely to have been degraded by the presence of the
uniform random dot texture. Furthermore, the shading cues they used were created with Brewsterian directional illumination, whereas our stimuli were constructed to simulate diffused illumination. The discrepancy between their and our results is consistent with the notion that the
shape-from-shading for a directional light source and for a diffuse light source are processed
by different modules in the visual system [30].

Conclusion

We show that the perceived depth modulation on a diffusely illuminated surface is consistent
with the prediction of an increase in proportion with the logarithm of luminance contrast. Perceived concaveness of the depth modulation depended on luminance phase but was essentially
unaffected by the luminance disparity modulation. The observers’ performance was consistent
with what one expected from the dark-is-deep rule for perceived depth from shading under diffuse illumination. Thus, these results validate the idea that human observers can use the diffuse
illumination assumption to perceive depth from luminance gradients alone without making
the assumption of light direction. Moreover, when combined with the disparity cues for the
same surface structure, the observers weighted luminance shading over the disparity information for depth judgments. This result cannot be explained by the maximum likelihood theory
of cue combination.

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Depth from Luminance Gradients

Fig 6. Example of the fit of the MLE model to the average data from Fig 2C (A) and Fig 3C (B).

doi:10.1371/journal.pone.0132658.g006

PLOS ONE | DOI:10 1371/journal pone 0132658 August 10 2015 15 / 17

Depth from Luminance Gradients

Acknowledgments

Support by Minister of Science and technology (Taiwan) 102-2420-H-002-018-MY3 to CCC.

Author Contributions

Conceived and designed the experiments: CCC CWT. Performed the experiments: CCC. Analyzed the data: CCC. Contributed reagents/materials/analysis tools: CCC CWT. Wrote the
paper: CCC CWT.

References

1. Horn BKP. Obtaining shape from shading information. The psychology of computer vision. New York:
McGraw Hill. 1975.

2. Horn BKP. Robot Vision. Cambridge, MA: MIT press. 1986.

3. Horn BKP, Brooks MJ. Shape from Shading. Cambridge, MA: MIT press. 1989.

4. Pentland AP. Finding the illuminant direction. Journal of the Optical Society of America. 1982; 72, 448–
455.

5. D’Zmura M. Shading ambiguity: Reflectance and illumination. In Landy MS & Movshon JA (eds.)
Computational Models of Visual Processing. MIT Press: Boston. 1991; 187–207.

6. Langer MS, Bulthoff HH. Depth discrimination from shading under diffuse lighting. Perception. 2000;
[29: 649–660. PMID: 11040949](http://www.ncbi.nlm.nih.gov/pubmed/11040949)

7. Brewster D. On the optical illusion of the conversion of cameos into intaglios, and of intaglios into cameos, with an account of other analogous phenomena. Edinburgh J Science. 1826; 4: 99–108.
(Reprinted in Wade NJ, Brewster and Wheatstone on Vision. NY: Academic Press. 1983).

8. Gregory RL. Eye and Brain: The Psychology of Seeing. Weidenfeld and Nicolson: London. 1966.

9. Ramachandran VS. Perceiving shape from shading. Scientific American. 1988; 259: 76–83. PMID:
[3064296](http://www.ncbi.nlm.nih.gov/pubmed/3064296)

10. Koenderink JJ, van Doorn AJ. Photometric invariants related to solid shape. Optica Acta. 1980; 27:
981–996.

11. Mingolla E, Todd JE. Perception of solid shape from shading. Biol Cybern. 1986; 53: 137–151. PMID:
[3947683](http://www.ncbi.nlm.nih.gov/pubmed/3947683)

12. Koenderink JJ, van Doom AJ. The shape of smooth objects and the way contours end. Perception.
1982; [11: 129–137 PMID: 7155766](http://www.ncbi.nlm.nih.gov/pubmed/7155766)

13. Reichel FD, Todd JT. Perceived depth inversion of smoothly curved surfaces due to image orientation.
J Exp Psychol Hum Percept Perform. 1990; [16: 653–64 PMID: 2144578](http://www.ncbi.nlm.nih.gov/pubmed/2144578)

14. Johnston A, Hill H, Carman N. Recognising faces: effects of lighting direction, inversion, and brightness
reversal. Perception. 1992; [21: 365–375. PMID: 1437456](http://www.ncbi.nlm.nih.gov/pubmed/1437456)

15. Hill H, Bruce V. Independent effects of lighting, orientation, and stereopsis on the hollow-face illusion.
Perception. 1993; [22: 887–97. PMID: 8190593](http://www.ncbi.nlm.nih.gov/pubmed/8190593)

16. Gerhard HE, Maloney LT. Estimating changes in lighting direction in binocularly viewed three-dimensional scenes. J Vis. 2010; 10: 14.

17. van Doorn AJ, Koenderink JJ, Wagemans J. Light fields and shape from shading. J Vision, 2011;
11:21, 1–21.

18. Gregory RL. The Intelligent Eye. London: Weidenfeld & Nicolson. 1970.

19. Ramachandran VS. Perception of shape from shading. Nature. 1988b; 331: 163–166.

20. Johnston A, Passmore P. Shape from shading. I: Surface curvature and orientation. Perception.1994;
[23: 169–189. PMID: 7971096](http://www.ncbi.nlm.nih.gov/pubmed/7971096)

21. Christou C, Koenderink JJ. Light source dependency in shape from shading. Vision Research. 1997;
[37:1441–1449. PMID: 9205707](http://www.ncbi.nlm.nih.gov/pubmed/9205707)

22. Khang BG, Koenderink JJ, Kappers AM. Shape from shading from images rendered with various surface types and light fields. Perception. 2007; [36: 1191–1213. PMID: 17972483](http://www.ncbi.nlm.nih.gov/pubmed/17972483)

23. Chen CC, Chen CM, Tyler CW. Depth structure from asymmetric shading supports face discrimination.
PLoS ONE. 2013; [8(2): e55865. doi: 10.1371/journal.pone.0055865 PMID: 23457484](http://dx.doi.org/10.1371/journal.pone.0055865)

24. Brewster D. Letters on Natural Magic. London: William Tegg.1868.

25. Sun J, Perona P. Where is the sun? Nature Neurosci. 1998; [1: 183–184. PMID: 10195141](http://www.ncbi.nlm.nih.gov/pubmed/10195141)

PLOS ONE | DOI:10 1371/journal pone 0132658 August 10 2015 16 / 17

Depth from Luminance Gradients

26. Mamassian P, Goutcher R. Prior knowledge on the illumination position. Cognition. 2001; 81: B1–B9.
[PMID: 11525484](http://www.ncbi.nlm.nih.gov/pubmed/11525484)

27. Langer MS, Zucker SW. Shape from shading on a cloudy day. J. Opt Soc of Amer A. 1994; 11, 467–
478.

28. Tyler CW. Diffuse illumination as a default assumption for shape-from-shading in the absence of shadows. J Inf Sci Tech. 1997; 42: 319–325

29. Wright M, Ledgeway T. Interaction between luminance gratings and disparity gratings. Spat Vis. 2004;
[17: 51–74. PMID: 15078012](http://www.ncbi.nlm.nih.gov/pubmed/15078012)

30. Schofield A. J., Rock P. B., Georgeson M. A. Sun and sky: Does human vision assume a mixture of
point and diffuse illumination when interpreting shape-from-shading? Vision Res. 2011; 51: 2317–
[2330. doi: 10.1016/j.visres.2011.09.004 PMID: 21945645](http://dx.doi.org/10.1016/j.visres.2011.09.004)

31. Sun P, Schofield AJ. Two operational modes in the perception of shape from shading revealed by the
effects of edge information in slant settings. J Vis. 2012; 12: 12, 1–12.

32. Pentland AP. "Shape information from shading: a theory about human perception." Spat Vis. 1989; 4:
[165–182. PMID: 2487162](http://www.ncbi.nlm.nih.gov/pubmed/2487162)

33. Marr D. Vision. Freeman: San Francisco. 1982.

34. Tyler CW. A stereoscopic view of visual processing streams. Vision Research. 1990; 30, 1877–1895.
[PMID: 2288096](http://www.ncbi.nlm.nih.gov/pubmed/2288096)

35. Bülthoff HH, Mallot HA. Integration of depth modules: stereo and shading. J Opt Soc Am A. 1988; 5:
[1749–58. PMID: 3204438](http://www.ncbi.nlm.nih.gov/pubmed/3204438)

36. Doorschot PC, Kappers AM, Koenderink JJ. The combined influence of binocular disparity and shading
on pictorial shape. Percept Psychophys. 2001; [63: 1038–1047. PMID: 11578048](http://www.ncbi.nlm.nih.gov/pubmed/11578048)

37. Todd JT, Norman JF, Koenderink JJ, Kappers AM. Effects of texture, illumination, and surface reflectance on stereoscopic shape perception. Perception. 1997; [26:807–22. PMID: 9509135](http://www.ncbi.nlm.nih.gov/pubmed/9509135)

38. Lee YL, Saunders JA. Stereo improves 3D shape discrimination even when rich monocular shape cues
are available. J Vis. 2011; [11, 6, doi: 10.1167/11.9.6](http://dx.doi.org/10.1167/11.9.6)

39. Lovell PG, Bloj M, Harris JM. Optimal integration of shading and binocular disparity for depth perception. J Vis. 2012; 12:1, 1–18.

40. Likova LT, Tyler CW. Peak localization of sparsely sampled luminance patterns is based on interpolated 3D surface representation. Vision Res. 2003; [43:2649–57. PMID: 14552806](http://www.ncbi.nlm.nih.gov/pubmed/14552806)

41. Tyler CW, McBride B. The Morphonome image psychophysics software and a calibrator for Macintosh
systems. Spatial Vision. 1997; [10: 479–484. PMID: 9176958](http://www.ncbi.nlm.nih.gov/pubmed/9176958)

42. Brainard DH. The Psychophysics Toolbox.Spatial Vision. 1997; [10: 433–436. PMID: 9176952](http://www.ncbi.nlm.nih.gov/pubmed/9176952)

43. Chen CC, Tyler CW. Evidence for elongated receptive field structure for mechanisms subserving stereopsis. Vision Res. 2006; [46: 2691–702. PMID: 16580041](http://www.ncbi.nlm.nih.gov/pubmed/16580041)

44. Pizlo Z, Li Y, Steinman RM. Binocular disparity only comes into play when everything else fails; a finding with broader implications than one might suppose. Spat Vis. 2008; [21: 495–508. doi: 10.1163/](http://dx.doi.org/10.1163/156856808786451453)
[156856808786451453 PMID: 19017479](http://dx.doi.org/10.1163/156856808786451453)

45. Norman JF, Clayton AM, Shular CF, Thompson SR. Aging and the perception of depth and 3-D shape
from motion parallax. Psychol Aging. 2004; [19: 506–14. PMID: 15383000](http://www.ncbi.nlm.nih.gov/pubmed/15383000)

46. Clark JJ, Yuille AL. Data fusion for sensory information processing systems. Boston: Kluwer Academic
Publishers. 1990.

47. Hillis JM, Watt SJ, Landy MS, Banks MS. Slant from texture and disparity cues: optimal cue combination. J Vis. 2004; [4: 967–992. PMID: 15669906](http://www.ncbi.nlm.nih.gov/pubmed/15669906)

48. MacAdam DL. Specification of small chromaticity differences. J Opt Soc Amer. 1943; 33, 18–26.

PLOS ONE | DOI:10 1371/journal pone 0132658 August 10 2015 17 / 17

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