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Perceiving translucent materials


Abstract and Figures

Many common materials, including fruit, wax and human skin, are somewhat translucent. What makes an object look translucent or opaque? Here we use a recently developed computer graphics model of subsurface light transport [Jensen, et al., 2001] to study the factors that determine perceived translucency. We discuss how physical factors, such as light-source direction can alter the apparent translucency of an object, finding that objects are perceived to be more translucent when illuminated from behind than in front. We also study the role of a range of image cues, including colour, contrast and blur, in the perception of translucency. Although we learn a lot about images of translucent materials, we find that many simple candidate sources of information fail to predict how translucent an object looks. We suggest that the visual system does not rely solely on these simple image statistics to estimate translucency: the relevant stimulus information remains to be discovered.
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Perceiving translucent materials
Roland W. Fleming1, Henrik Wann Jensen2 & Heinrich H Bülthoff1
1Max Planck Institute for Biological Cybernetics.
2Computer Graphics Laboratory, UCSD.
Many common materials, including fruit, wax and human skin,
are somewhat translucent. What makes an object look
translucent or opaque? Here we use a recently developed
computer graphics model of subsurface light transport [Jensen, et
al., 2001] to study the factors that determine perceived
translucency. We discuss how physical factors, such as light-
source direction can alter the apparent translucency of an object,
finding that objects are perceived to be more translucent when
illuminated from behind than in front. We also study the role of
a range of image cues, including colour, contrast and blur, in the
perception of translucency. Although we learn a lot about
images of translucent materials, we find that many simple
candidate sources of information fail to predict how translucent
an object looks. We suggest that the visual system does not rely
solely on these simple image statistics to estimate translucency:
the relevant stimulus information remains to be discovered.
CR Categories: J.4 [Computer Application]: Social and
Behavioral Sciences; I.3.7. [Computer Graphics] Three-
dimensional Graphics and Realism.
Keywords: subsurface scattering; perception; psychophysics.
1. Introduction
Many materials that we commonly encounter are
translucent, including leaves and fruit flesh; textiles and papers;
various stones, such as agate or marble; soaps; wax; some types
of glass; milk and human skin. How do we recognize these
materials? What image cues allow us to tell that a surface is
translucent rather than opaque? Here we use a combination of
psychophysics and computational image analysis to study the
image properties that underlie the distinctive appearance of
translucent materials.
When light strikes a translucent material, it enters the body
of the object, scatters, and re-emerges from the surface. The
light that bleeds through translucent objects gives them a
characteristic softness and glow. However, although light is
visible through translucent objects, form is generally not. This
has profound consequences for the image cues underlying
translucency perception, and results from the way that light
scatters beneath the surface of an object.
Subsurface light transport
Recent advances in computer graphics [Jensen et al. 2001]
allow us to simulate translucent materials realistically and
efficiently. In translucent materials light scatters below the
surface of the object. This phenomenon, called subsurface
scattering, causes light to spread beneath the surface and
reemerge in a region around the point of illumination. In contrast,
traditional models for light scattering based on the Bidirectional
Reflectance Distribution Function (BRDF) assume that materials
are opaque, and that all light is reflected from the point that is
illuminated (Figure 1).
(a) Surface reflection (BRDF)
(b) Subsurface scattering (BSSRDF)
Figure 1. The optical behaviour of reflective and translucent
A full simulation of translucency requires solving the
radiative transport equation [Chandrasekhar, 1961]. Jensen et al.
[2001] simplify the light scattering by assuming that the
translucent material is homogeneous. In this case the scattering
of light can be approximated by a diffusion equation as well as a
term for single scattering. These terms form a Bidirectional
Scattering Surface Reflectance Distribution Function (BSSRDF).
Figure 2. The laughing Buddha rendered with different settings of the BSSRDF. The scattering coefficient progressively
increases from left to right, all other parameters are held constant. The apparent material quality ranges from glassy, through
jade-like to porcelain. Model courtesy of the Stanford 3D scanning repository [].
The parameters in the BSSRDF are the refractive index of
the material, the phase function (Jensen et al. [2001] use the
Henyey-Greenstein [1941] phase function), and the scattering
and absorption coefficients. The absorption and scattering
coefficients specify the probability that a photon will be absorbed
or scattered when traveling a given distance within the material.
In most materials these parameters determine the color as well as
the degree of translucency. This is particularly the case in highly
scattering materials, where the diffusion term dominates [Jensen
and Buhler 2002], and the phase function can be mostly ignored.
Koenderink and van Doorn [2001] pointed out that
translucent material have an appearance that differs from
traditional Lambertian materials, and they investigated the
diffusion of light in simple geometric shapes in order to analyze
the shape from shading characteristics of translucent objects.
They observed that the shape of translucent objects is difficult to
analyze as the appearance depends not only on the location of the
lighting and the observer, but also on the shape of the object,
since most of the lighting is due to scattering within the
translucent object.
In this paper we study the effect of the absorption and the
scattering coefficient on the perceived appearance of translucent
materials. We also study the effect of other components such as
highlights and demonstrate how these parameters significantly
influence our perception of the material. An example is shown in
Figure 2, where a Buddha statue is changed from highly
translucent to mostly opaque. Changes in translucency can
influence apparent softness, realism and even how edible an
object looks.
2. Physical factors that influence the impression of
Although the parameters of the BSSRDF can dramatically
alter the appearance of a material, we have found that other
factors can also influence how translucent an object looks. Here
we discuss physical factors and viewing conditions that affect
perceived translucency.
Highlights and translucency
Figure 3. The role of highlights in the perception of
translucency. Left images have no highlights, right images
have highlights. Most observers agree that the specularities
make the impression of translucency more compelling.
Highlights occur when light is specularly reflected from the
surface of an object. Note that specular reflections are caused by
the interface between two materials of different refractive index
(i.e. the surface itself). As such, highlights are not a direct
consequence of light transport within the body of an object. We
might therefore expect the perception of translucency to be quite
independent of specular reflections1.
However, many translucent materials that we commonly
encounter are somewhat glossy (e.g. plastic, wax or marmalade).
This means that the human visual system may ‘expect’
translucent materials to exhibit specular reflections.
Interestingly, we have found that highlights can contribute
to the visual impression of translucency. Consider, for example,
the images shown in Figure 3. Each pair of images has been
rendered under identical conditions. The only difference
between the images is the presence or absence of specular
highlights. Observers generally agree that the glossy surfaces
look more like canonical translucent materials than the surfaces
without highlights.
It is worth noting that the visual system tolerates physical
inconsistencies between the transmitted and reflected
components of the image. For example, in image 3b, the
translucent component was rendered under a single point light
source from in front, while the specular component was rendered
under full-scene illumination from the Debevec [1998]
panoramic light probe database. The dominant illumination
direction in this light probe is from the left. Despite the
inconsistency, we readily fuse the two components into a single
percept of a glossy, translucent object. The inconsistency in
illumination does not hinder the sense of translucency. On the
contrary, observers generally agree that the inconsistently
illuminated image looks more realistic than its consistent, but
highlight-less counterpart.
Effects of light-source direction
Anyone who has spent time playing with translucent
materials as a child, will have noticed that they tend to look more
translucent when held up to the light. When illuminated from
behind, a gemstone or slice of fruit is filled with a distinctive
glow, which enhances the sense of the object’s translucency.
An example of this effect is shown in Figure 4. A torus is
illuminated from six different orientations (three behind and
three in front). Note that the apparent translucency of the torus
changes depending on the light direction. We have studied this
effect systematically, using a psychophysical translucency-
matching task.
Fifteen subjects were presented with images of two
translucent tori simultaneously. On each trial the image on the
left (the “Test” image) was selected by the computer, while the
image on the right (the “Match”) could be adjusted by the
subject. The two objects were illuminated from different
directions. The subject’s task was to adjust the translucency of
the Match image until the torus appeared to be made out of the
same material as the Test torus, despite differences in the
The translucency of the Test object could be one of three
different values, ranging from highly translucent—like jade—to
highly opaque2. We tested the appearance of these three objects
under 12 different illumination directions around the object.
1 Note, however, that the binocular depth of highlights is
generally not on the surface itself [Blake and Bülthoff 1990,
1991]. Here we are dealing with monocular images, but it is
possible that adding stereoscopic information would alter the
effects described here.
2 Specifically, the absorption coefficient was held fixed at 0.1
while the scattering coefficient was set to 2.00, 7.13 or 20.0.
Figure 4. A translucent torus illuminated from six
different directions. Top row, illumination from behind.
Bottom row, illumination from in front. Note that the
apparent opacity of the torus changes depending on the
direction of illumination.
The Match object was always illuminated from the same
direction (back left), but the subject could adjust the apparent
translucency through 128 different values, which spanned a range
greater than the three values used for the Test objects.
Specifically, the absorption coefficient was held fixed at 0.1 (i.e.
the same as the Test objects), while the subject adjusted the
scattering coefficient through 128 steps from 0.4 to 60. The step
sizes were non-linear to create a more uniform perceptual scale.
Subjects readily agreed with the statement “when I move the
mouse to the right, the torus appears to change from being
relatively translucent to relative opaque, while everything else
about the scene appears to stay the same”. Therefore, for
subsequent discussion, we refer to this modified scattering
coefficient scale as “perceived opacity”.
All the Test images were shown to the subjects twice in a
randomly interleaved sequence. Subjects were given unlimited
time to adjust the Match stimulus using the mouse, and once
satisfied with the match, could move onto the next trial in the
sequence by pressing a button on the keyboard.
Before the main experiment, subjects were given practice
trials consisting of the same three Test objects illuminated from 9
different directions. During practice, subjects were asked to say
out loud a numerical rating of the perceived opacity on a scale
from 1-7 before adjusting the mouse. No feedback was given, the
practice was simply intended to help subjects orient to the task.
Figure 5. Mean data across 15 subjects. Perceived
opacity varies as a function of light source direction.
Angles less than 180 degrees corresponding to lighting
from behind the object, while angles greater than 180
are illuminated from in front. Error bars represent
standard error.
The mean data across all subjects is shown in Figure 5.
Note that if the observers were able to accurately estimate the
intrinsic parameters of the BSSRDF irrespective of the
illumination, the data would fall along the three horizontal lines.
This is not the case. Instead, perceived opacity undergoes a
dramatic change when the lighting is altered. All three objects
appeared significantly more opaque when illuminated from in
front than from behind. The effect is most marked for the object
of intermediate translucency.
Thus, for the conditions used in our experiment, the visual
system seems poor at ‘discounting’ the effects of light source
direction. Objects tend to appear more translucent when
illuminated from behind. It is interesting to contrast this with
previous work on the perception of other material attributes. For
example, Fleming, Dror and Adelson [2002] found that the
perception of gloss remains relatively stable across change in
illumination, as long as the pattern of illumination is realistic.
One consequence of the current finding is that if we wish to
enhance or emphasise the apparent translucency of an object, for
example in an animation, we should organize the scene lighting
so that the object is illuminated predominantly from behind.
3. Image measurements
If we wish to understand how the visual system estimates
translucency, we must identify the image cues that carry
information about translucency. To this end, we have measured
how various image properties vary with parameters of the
BSSRDF. We describe a number of these measurements here.
Colour saturation
How does colour influence perceived translucency? When
white light passes through a coloured translucent object, it is
progressively filtered, and emerges coloured. Interestingly, hue,
saturation and intensity can all vary as a function of the distance
travelled by a ray through a translucent material. The BSSRDF
can produce a wide range of colour phenomena, depending on
the colour values of the scattering and absorption coefficients. A
few examples are shown in Figure 6 (see colour plate).
We know that colour is not necessary for the perception of
translucency, because a black-and-white photograph can
nevertheless yield a vivid impression of translucency. However,
can colour modify the sense of translucency when present?
We have found that colour saturation can affect the way a
translucent object appears to ‘glow’. Consider the two cubes in
Figure 7 (see colour plate). The hue and intensity components of
the two images are identical, what differs is the saturation
component. In (a) the saturation is positively correlated with the
intensity image, while in (b) it is negatively correlated. The mean
saturation is held constant. Observers generally agree that (a)
appears to have a ‘warmer’ glow, while (b) appears more ‘icy’ or
Figure 7. (a) Colour saturation is positively correlated
with intensity. (b) saturation is negatively correlated
with intensity. Mean saturation is identical for the two
images. Most observers agree that (a) appears ‘warmer’
than (b).
Although saturation variations can affect perceived
translucency, they are insufficient on their own to yield an
impression of translucency. This is demonstrated in Figure 8 (see
colour plate). In (a), saturation is held constant across the image,
while intensity varies. In (b), intensity is held constant while
saturation varies. Most subjects agree that (a) looks translucent,
while (b) does not. This suggests that the saturation component is
neither necessary nor sufficient to yield an impression of
Figure 8. (a) Intensity varies across the image but
saturation is constant. (b) intensity is constant but
saturation varies. Most observers agree that (a) looks
more translucent than (b)
Image contrast
Light diffuses through translucent materials, much like dye
diffusing through a fluid. When illuminated translucent objects
become ‘filled’ with light. An important consequence of this is
that points on the surface that do not receive any direct
illumination (i.e. they are in shadow) can nevertheless receive
light from within the body of the object. Conversely, regions that
receive strong direct illumination tend to dissipate the incident
light by transmitting it to other parts of the object. This has the
effect of reducing the overall contrast of translucent objects.
Figure 9 shows an example of this. Three objects are shown
under the same lighting. Torus A is the most translucent, Torus
B is of intermediate translucency, while Torus C is relatively
opaque. Note that the range of intensities in the images
progressively increases from A to C. It seems reasonable, then,
that the human visual system might use image contrast to
estimate the opacity of an object. In this section, we discuss the
role of image contrast in the perception of translucency.
Figure 9. Three tori in increasing order of opacity.
Note that the range of intensities in the images increases
from A to C.
How should we define contrast? It is common to alter the
contrast and brightness of an image using a linear (affine)
transformation of the image intensities. Specifically, to adjust
contrast, intensities are multiplicatively scaled, while to adjust
brightness, image intensities are additively scaled. Can we use
the concept of linear transformations to understand the
relationship between opaque and translucent objects?
Figure 10 directly compares Torus A with Torus C. If we
plot the intensities of the translucent torus on the x-axis, and the
intensities of corresponding locations in the opaque torus on the
y-axis, we see that the two images are not linearly related to one
Figure 10. The non-linear relationship between
intensity values in Torus A and Torus C.
This is important because it affects our concept of contrast.
Specifically, it excludes any definition that assumes linearity.
This is highlighted if we take Torus B, and try to adjust the image
intensities to make it appear like Torus A or Torus C. If we
modify the image intensities according to the best-fitting linear
transform, we get the results shown in Figure 11.
Figure 11. Here we linearly adjust the intensities of
Torus B to try to make the image appear as translucent
as Torus A and as opaque as Torus C.
Although these transforms offer the best compromise
between matching the brightness and contrast of the images, we
see that they fail to match the apparent translucency of the
objects. This is especially clear for the low-contrast image, which
looks considerably less translucent than the target image (Torus
When we gradually alter an object from translucent to
opaque, we see that the entire distribution of image intensities
changes shape (Figure 12). The mode shifts to lower intensities,
while the whole distribution becomes more skewed. This
Torus A
Torus B
Torus C
Torus B linearly adjusted to
match contrast of Torus A
Torus B linearly adjusted to
match contrast of Torus C
suggests that our concept of contrast should take into account the
entire distribution of intensities, or at least some summary
statistics (e.g. mode and skew), that capture these changes.
Figure 12. Intensity histograms of the three images.
Note that the entire shape of the distribution changes as
the torus becomes more opaque.
This is highlighted if we try to match Torus B to the other
two images by adjusting the entire distribution of lights and
darks. The results of this histogram matching process are shown
in Figure 13. Note that the adjusted images look considerably
closer to their targets than for the linear transformations in Figure
It is important to note that histogram matching preserves the
ordinal relationship between pixels, so the spatial structure of the
image is not affected by the non-linear transformation.
Histogram matching cannot, for example, introduce blurriness to
the image, or change the position of highlights. Subsurface
scatter does introduce blur to the image, as we discuss below.
Thus, it is all the more surprising we can make a translucent
object appear opaque simply by changing the intensity
However, although the intensity histogram captures
something important about translucent objects, we should
emphasise that it is insufficient alone. For example, in Figure 14,
the pixels in Torus A have been scrambled (in a way that keeps
the image quite smooth) to create a pattern of random noise with
the same intensity histogram as Torus A. Unsurprisingly, this
transformation destroys the impression of translucency. It is
worth noting that the scrambling also destroys the sense of three-
dimensional shape. It is possible that by using a different
scrambling procedure that preserves the sense of three-
dimensionality, the impression of translucency would persist.
Clearly it is not just the distribution of lights and darks, but their
spatial relations that inform us that something is translucent.
Furthermore, intensity distributions can, of course, be
affected by factors other than the degree of translucency. For
example, moving the light source, alters the proportion of the
object that is in shadow. This naturally changes ratio of light to
dark in the image. As we discussed above, perceived
translucency does change when the light source moves.
Unfortunately, however, these changes cannot be easily predicted
solely from the changes in the intensity histogram.
Figure 13. Here we use histogram matching to adjust the
intensities in Torus B to make the image appear as Torus
A and as opaque as Torus C.
In summary, a simple linear concept of contrast cannot
account for the different appearances of translucent and opaque
objects. If, instead, we consider the full intensity distribution, we
can traverse the space of translucent and opaque objects
surprisingly successfully, as long as the lighting conditions are
held constant. However, the intensity distribution fails to capture
crucial information about the spatial structure of translucent
Figure 14 A noise pattern that has the same intensity
histogram as Torus A. The image does not appear
Torus B non-linearly adjusted to
match histogram of Torus A
Torus B non-linearly adjusted to
match histogram of Torus C
Spatial structure: blur and isophotes
We have argued that it is not solely the proportion of light
and dark in an image, but their spatial relationships that makes an
object look translucent. In this section we discuss how
translucency affects the spatial structure of images.
Subsurface scatter causes light rays to spread out into hazy
puffs as they pass through a translucent medium. This has the
effect of blurring out sharp details in the image of a translucent
object. Consider, for example, the images in Figure 1. Details
that are visible in the most opaque Buddha become softened or
even invisible in the more translucent objects. Note also the
edges of the shadows cast across the tori in Figure 9. These are
crisp and pronounced in Torus C, but blurry and diffuse in Torus
A. Blur evidently plays a key role in the distinctive soft
appearance of translucent materials.
We have found, however, that blur is insufficient on its own
to produce a percept of translucency. If the intensity distribution
is held roughly constant, adding blur by itself has little effect on
perceived translucency (Figure 15). This makes sense given that
many other factors can produce blur in images, including depth
of field effects and shadow penumbras.
Figure 15. Effects of blur on perceived translucency.
(a) Original version of Torus B. (b) Blurred version
of Torus B. (c) Blurriness in the more translucent
Torus A. Note that the blur has almost no effect on
the apparent translucency of (b), although the blur is
clearly visible in the close-up.
More generally, it is interesting to study the structure of
images of translucent objects. The spatial organisation of lights
and darks in an image can be made visible by plotting the
isophotes (contours of equal luminance), as shown in Figure 16.
Note the fact that the isophotes are closer together in Torus B than
in Torus A. This is related to the smoothness of Torus A, another
way of conceiving of blur.
(a) Torus A (b) Torus B
Figure 16. Isophotes of Torus A and B. Note the
bunching up around the edge of the shadow in Torus
It is important to note that spatial organization alone does not
tell us whether an object is translucent or not. This is
demonstrated in Figure 17, which shows the photographic
negative of Torus A. The photographic negative has exactly the
same pattern of isophotes as its positive counterpart, and yet the
image does not appear translucent. This is important as it
suggests that the visual system attends to the direction in which
intensity is varying, and not just to the spatial layout of the
intensity variations.
(a) Negative of Torus A (b) Isophotes
Figure 17. The photographic negative of Torus A has
the same isophote pattern, but does not appear
4. Conclusions
What makes wax, cheese or marble look translucent? Here
we have used a combination of psychophysics and image analysis
to study some of the factors that influence perceived translucency.
To summarize our findings, let us conclude with some
suggestions for artists who wish to render translucent materials.
Translucent objects look most translucent when they are
glossy and lit from behind. Glossiness also aids the perception of
shape, by recovering detail that is lost by the softening effects of
subsurface scatter.
If we wish translucent objects to look ‘glowing’ and ‘warm’,
colour saturation should be positively correlated with intensity.
By contrast, if we wish them to look ‘icy’ or ‘dilute’, the
correlation should be negative.
Translucent objects should be lower contrast than opaque
ones. However, the relationship between translucent and opaque
versions of an object is generally non-linear. Thus, to portray a
translucent object realistically, it is not sufficient simply to reduce
an opaque object’s contrast (using Photoshop, for example). It is
necessary to modify the entire distribution of intensities.
Sharp cast shadows should be avoided as they make objects
appear hard and opaque, while blur and loss of detail gives
translucent objects their characteristic soft appearance. However,
as with contrast, we cannot make an opaque object appear
translucent simply by blurring out the details. Attention must be
paid to the global effects of light bleeding through the object if
we wish to portray a translucent material.
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... Cette thérapeutique, bien qu'ingénieuse, nécessite au préalable, selon la topographie de la lésion, la ''mise à nue'' de celle-ci par des moyens chimiques ou mécaniques, plus ou moins invasifs. (Figure 4) Figure 4 : (1, 2) Hypominéralisation affectant le tiers cervical de lʼincisive centrale (3,4,5,6) Phase dʼexposition à la lésion (2,8) Phase de comblement de la lésion par la résine infiltrante. ...
... Ainsi, une lésion sera d'autant plus profonde que ses bords sont flous et inversement d'autant plus superficielle que les bords sont nets. [8] [9] Alors que leurs association traduira sur le plan topographique la présence d'une lésion mixte. [10] [11] Une démarche thérapeutique à chaque localisation topographie. ...
... Cette thérapeutique, bien qu'ingénieuse, nécessite au préalable, selon la topographie de la lésion, la ''mise à nue'' de celle-ci par des moyens chimiques ou mécaniques, plus ou moins invasifs. (Figure 4) Figure 4 : (1, 2) Hypominéralisation affectant le tiers cervical de lʼincisive centrale (3,4,5,6) Phase dʼexposition à la lésion (2,8) Phase de comblement de la lésion par la résine infiltrante. ...
... Ainsi, une lésion sera d'autant plus profonde que ses bords sont flous et inversement d'autant plus superficielle que les bords sont nets. [8] [9] Alors que leurs association traduira sur le plan topographique la présence d'une lésion mixte. [10] [11] Une démarche thérapeutique à chaque localisation topographie. ...
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Dans notre pratique quotidienne, les taches blanches de l’émail sont devenues un motif de consultation de plus en plus fréquent. Cependant, de nos jours, il existe plusieurs options thérapeutiques qui visent à traiter ce type de lésion. Le choix de l’une ou l’autre de ces thérapeutiques repose sur leur mécanisme d’action d’une part, et des particularités histopathologiques de ces lésions d’autre part. Par conséquent, la compréhension de ces lésions d’un point de vue anatomopathologie ainsi que le mode d’action des différentes solutions thérapeutiques est nécessaire afin de porter son choix sur la thérapeutique optimale. Dans cet article, nous proposons à travers des cas cliniques une démarche diagnostique et thérapeutique pour la gestion des taches blanches de l’email.
... cues that the visual system extracts from the scene, which act as heuristics rather than deterministic cues to the physical properties being perceived - Chadwick and Kentridge, 2015), which are indirectly driven by changes in the underlying physical parameters, but that can also encapsulate complex interactions of light transport within the material. Factors affecting perceived translucence include the amount of light absorption (darkness), the direction of illumination, the shape and size of the object, and colour (Fleming and Jensen, 2004;Fleming and Bülthoff, 2005;Fleming, 2014). Human observers are capable of identifying materials within fractions of a second (Sharan et al., 2009), and can readily discriminate visually between very similar translucent materials such as skin or fruit and their waxwork replicas, despite the subtlety of such differences. ...
... There has, however, been little focus on the cortical areas that may be involved in human translucence perception. As we appear to be so proficient at detecting differences in translucence (Jensen et al., 2001, Vasseleu, in Cubitt et al., 2015, p. 163-178, and Murakoshi et al., 2013, and translucence seems to be associated with important properties of natural materials such as the health of skin or ripeness of fruit (Hetherington et al., 1990;Fleming et al., 2004;Fleming and Bülthoff, 2005), it is plausible to ask whether there is a region of cerebral cortex specialised for processing translucence. ...
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Translucence is an important property of natural materials, and human observers are adept at perceiving changes in translucence. Perceptions of different material properties appear to arise from different cortical regions, and it is therefore plausible that the perception of translucence is dependent on specialised regions, separate from those important for colour and texture processing. To test for anatomical independence between areas necessary for colour, texture and translucence perception we assessed translucency perception in a cortically colour blind observer, who performs at chance on tasks of colour and texture discrimination. Firstly, in order to establish that MS has shown no significant recovery, we assessed his colour perception performance on the Farnsworth-Munsell 100 Hue Test. Secondly, we tested him with two translucence ranking tasks. In one task, stimuli were images of glasses of tea varying in tea strength. In the other, stimuli were glasses of tea varying only in milkiness. MS was able to systematically rank both strength and milkiness, although less consistently than controls, and for tea strength his rankings were in the opposite order. An additional group of controls tested with greyscale versions of the images succeeded at the tasks, albeit slightly less consistently on the milkiness task, showing that the performance of normal observers cannot be transformed into the performance of MS simply by removing colour information from the stimuli. The systematic performance of MS suggests that some aspects of translucence perception do not depend on regions critical for colour and texture processing.
... The diffusion equation is a well-known approximation technique, that is used to represent homogeneous translucent materials [FJB04]. The most of the homogeneous subsurface scattering models used in the computer graphics community are derived based on this approximation [JMLH01, JB02, FHK14, JZJ * 15]. ...
Conference Paper
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In this paper, we present a novel heterogeneous subsurface scattering (sss) representation, which is based on a combination of Singular Value Decomposition (SVD) and genetic optimization techniques. To find the best transformation that is applied to measured subsurface scattering data, we use a genetic optimization framework, which tries various transformations to the measured heterogeneous subsurface scattering data to find the fittest one. After we apply the best transformation, we compactly represent measured subsurface scattering data by separately applying the SVD per-color channel of the transformed profiles. In order to get a compact and accurate representation, we apply the SVD on the model errors, iteratively. We validate our approach on a range of optically thick, real-world translucent materials. It's shown that our genetic algorithm based heterogeneous subsurface scattering representation achieves greater visual accuracy than alternative techniques for the same level of compression.
... Due to the complexity of translucent materials, representing these is commonly simplified by assuming that the optical properties of the material are constant (homogeneous). In this case, light scattering can be approximated by a diffusion equation and a term for single scattering [9]. Therefore, many successful homogeneous subsurface scattering models use this approximation [6,10,17,18,21]. ...
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We present a novel genetic algorithm-based approach for the compact representation of heterogeneous, optically thick, translucent materials. Utilizing genetic optimization, we also find the best transformation to represent measured subsurface scattering data. We employ a factored subsurface scattering representation, based on a singular value decomposition (SVD), separately applying the SVD per-color channel of the transformed profiles. In order to achieve a compact, accurate representation, we perform this iteratively on the model errors. By allowing the number of iterations to be customized, our representation provides a mechanism to trade the visual quality possible against the level of compression achieved through our representation. We validate our approach by analyzing a range of real-world translucent materials, geometries and lighting conditions. For heterogeneous translucent materials, we further demonstrate that for the same level of compression, our method achieves greater visual accuracy than alternative techniques. Finally, we present an application of our factored representation, which can be used to convert heterogeneous materials into homogeneous material representations.
... However, in rendering the complex scanned data of objects made from translucent materials, blurred geometric details become more pronounced as the illuminant direction approaches a horizontal orientation. According to previous works [1,2], highlights can contribute to the visual impression of translucency and observers can use the shape information of highlights alone to estimate curvature magnitude. In 3D color printing, a high level of detail and realism should be exhibited for the purpose of reproducing complex appearance properties and more control over the internal structure [3]. ...
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The acquisition of translucent objects has become a very common task thanks to the progress of 3D scanning technology. Since the characteristic soft appearance of translucent objects is due to subsurface scattering, the details are naturally left out in this appearance. For objects that have complex shapes, this lack of detail is obviously more prominent. In this paper, we propose a method to preserve the details of surface geometry by adding highlight effects. In generating highlight effects, our method employs a local orthonormal frame and combines, in a novel way, the incoming and outgoing light in approximating the subsurface scattering process. When the incident illuminant direction changes from nearly overhead to nearly horizontal, our method effectively preserves complex surface geometry details in the appearance of translucent materials. Through experiments, we show that our method can store surface features as well as maintain the translucency of the original materials and even enhance the perception of translucency. By numerically comparing the generated highlight effects with those generated by the traditional Bidirectional Reflectance Distribution Function (BRDF) models with different bandwidth parameters, we demonstrate the validity of our proposed method.
... Translucency is often viewed as the intermediate between transparency and opacity. Among the rare studies dedicated to this concept, the one by Fleming et al. [1] addresses the opposition between translucency and opacity through image synthesis. For example, the authors show that translucency of smooth objects is more noticeable when the object is illuminated from behind than when illuminated on the front side. ...
Conference Paper
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Translucency is a visual property attributed to objects that light may cross without transmitting a clear image of the scene which is behind. In absence of a more precise definition, this perceptual attribute is often considered as an intermediate between transparency, which is the property of objects that light may cross by transmitting a clear image of the scene behind, and opacity, which is the property of blocking the transmission of light and therefore masking completely the scene behind. If it is rather clear that translucency is closely related to light scattering, it is difficult to classify the translucent appearance according to one scale only, due to the different types of scattering, which can occur as well as the role of absorbance and thickness of the material. Through synthetic images rendered by optical models, we show that surface scattering, volume (or subsurface) scattering, possibly mixed with selective absorption, produce different types of translucency effects and different intermediates between transparency and opacity. We thus propose to represent translucency according to three axes related to these three optical phenomena: surface scattering, volume scattering, and absorption.
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When judging optical properties of a translucent object, humans often look at sharp geometric features such as edges and thin parts. Analysis of the physics of light transport shows that these sharp geometries are necessary for scientific imaging systems to be able to accurately measure the underlying material optical properties. In this paper, we examine whether human perception of translucency is likewise affected by the presence of sharp geometry, by confounding our perceptual inferences about an object's optical properties. We employ physically accurate simulations to create visual stimuli of translucent materials with varying shapes and optical properties under different illuminations. We then use these stimuli in psychophysical experiments, where human observers are asked to match an image of a target object by adjusting the material parameters of a match object with different geometric sharpness, lighting geometry, and 3D geometry. We find that the level of geometric sharpness significantly affects perceived translucency by the observers. These findings generalize across a few illuminations and object shapes. Our results suggest that the perceived translucency of an object depends on both the underlying material optical parameters and 3D shape. We also conduct analyses using computational metrics including (luminance-normalized) L2, structural similarity index (SSIM), and Michelson contrast. We find that these image metrics cannot predict perceptual results, suggesting low level image cues are not sufficient to explain our results.
Color texture reproduction in 3D printing commonly ignores volumetric light transport (cross-talk) between surface points on a 3D print. Such light diffusion leads to significant blur of details and color bleeding, and is particularly severe for highly translucent resin-based print materials. Given their widely varying scattering properties, this cross-talk between surface points strongly depends on the internal structure of the volume surrounding each surface point. Existing scattering-aware methods use simplified models for light diffusion, and often accept the visual blur as an immutable property of the print medium. In contrast, our work counteracts heterogeneous scattering to obtain the impression of a crisp albedo texture on top of the 3D print, by optimizing for a fully volumetric material distribution that preserves the target appearance. Our method employs an efficient numerical optimizer on top of a general Monte-Carlo simulation of heterogeneous scattering, supported by a practical calibration procedure to obtain scattering parameters from a given set of printer materials. Despite the inherent translucency of the medium, we reproduce detailed surface textures on 3D prints. We evaluate our system using a commercial, five-tone 3D print process and compare against the printer's native color texturing mode, demonstrating that our method preserves high-frequency features well without having to compromise on color gamut.
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Images of artificial and natural scenes typically contain many highlights generated by mirror-like reflection from glossy surfaces. Until recently, computational models of visual processes have tended to regard highlights as obscuring the structure of the underlying scene. The truth is that, on the contrary, highlights are rich in local geometric information. Here we report that the three-dimensional appearance of a highlight on a computer-simulated stereoscopic curved surface affects observers' judgment of surface gloss. We also show that the 3-D appearance of a highlight affects the perception of surface curvature--that is, it can force an ambiguous convex-concave figure to change state. We thus conclude that human visual analysis seems to employ a physical model of the interaction of light with curved surfaces, a model firmly based on ray optics and differential geometry.
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Under typical viewing conditions, we find it easy to distinguish between different materials, such as metal, plastic, and paper. Recognizing materials from their surface reflectance properties (such as lightness and gloss) is a nontrivial accomplishment because of confounding effects of illumination. However, if subjects have tacit knowledge of the statistics of illumination encountered in the real world, then it is possible to reject unlikely image interpretations, and thus to estimate surface reflectance even when the precise illumination is unknown. A surface reflectance matching task was used to measure the accuracy of human surface reflectance estimation. The results of the matching task demonstrate that subjects can match surface reflectance properties reliably and accurately in the absence of context, as long as the illumination is realistic. Matching performance declines when the illumination statistics are not representative of the real world. Together these findings suggest that subjects do use stored assumptions about the statistics of real-world illumination to estimate surface reflectance. Systematic manipulations of pixel and wavelet properties of illuminations reveal that the visual system's assumptions about illumination are of intermediate complexity (e.g., presence of edges and bright light sources), rather than of high complexity (e.g., presence of recognizable objects in the environment).
Shading is an important shape cue. Theories of 'shape from shading' assume that the shading is due to collimated beams irradiating opaque smooth Lambertian surface. Many objects are not at all opaque though. In cases of translucent objects photons penetrate the surface and enter the volume of the object, perhaps to re-emerge from the surface at another location. In such cases there can be no 'shading' proper. In the limit of very strong scattering these materials approach opaque Lambertian surfaces, in the limit of very weak scattering they approach transparent objects such as glass or water. A general theory of 'shading' in the case of translucent objects is not available. We study the optical properties for a number of geometries. In simple cases the scattering problem can be solved and we obtain models of 'shading' of translucent material that are distinct from the opaque Lambertian case. In more general cases one needs to make certain approximations. We show how to develop rules of thumb for generic cases. Such rules are likely candidates for models of human visual perception of wrinkles in human skin or articulations of cumulus clouds.
We present a method that uses measured scene radiance and global illumination in order to add new objects to light-based models with correct lighting. The method uses a high dynamic range imagebased model of the scene, rather than synthetic light sources, to illuminate the newobjects. To compute the illumination, the scene is considered as three components: the distant scene, the local scene, and the synthetic objects. The distant scene is assumed to be photometrically unaffected by the objects, obviating the need for re- flectance model information. The local scene is endowed with estimated reflectance model information so that it can catch shadows and receive reflected light from the new objects. Renderings are created with a standard global illumination method by simulating the interaction of light amongst the three components. A differential rendering technique allows for good results to be obtained when only an estimate of the local scene reflectance properties is known. We apply the general method to the problem of rendering synthetic objects into real scenes. The light-based model is constructed from an approximate geometric model of the scene and by using a light probe to measure the incident illumination at the location of the synthetic objects. The global illumination solution is then composited into a photograph of the scene using the differential rendering technique. We conclude by discussing the relevance of the technique to recovering surface reflectance properties in uncontrolled lighting situations. Applications of the method include visual effects, interior design, and architectural visualization.
Images of artificial and natural scenes typically contain many 'specularities' generated by mirror-like reflection from glossy surfaces. Until fairly recently computational models of visual processes have tended to regard specularities as obscuring underlying scene structure. Mathematical modelling shows that, on the contrary, they are rich in local geometric information. Recent psychophysical findings support the notion that the brain can apply that information. Our results concern the inference of 3D structure from 2D shaded images of glossy surfaces. Stereoscopically viewed highlights or 'specularities' are found to serve as cues for 3D local surface-geometry.
This paper introduces a simple model for subsurface light transport in translucent materials. The model enables efficient simulation of effects that BRDF models cannot capture, such as color bleeding within materials and diffusion of light across shadow boundaries. The technique is efficient even for anisotropic, highly scattering media that are expensive to simulate using existing methods. The model combines an exact solution for single scattering with a dipole point source diffusion approximation for multiple scattering. We also have designed a new, rapid image-based measurement technique for determining the optical properties of translucent materials. We validate the model by comparing predicted and measured values and show how the technique can be used to recover the optical properties of a variety of materials, including milk, marble, and skin. Finally, we describe sampling techniques that allow the model to be used within a conventional ray tracer.
This paper introduces an efficient two-pass rendering technique for translucent materials. We decouple the computation of irradiance at the surface from the evaluation of scattering inside the material. This is done by splitting the evaluation into two passes, where the first pass consists of computing the irradiance at selected points on the surface. The second pass uses a rapid hierarchical integration technique to evaluate a diffusion approximation based on the irradiance samples. This approach is substantially faster than previous methods for rendering translucent materials, and it has the advantage that it integrates seamlessly with both scanline rendering and global illumination methods. We show several images and animations from our implementation that demonstrate that the approach is both fast and robust, making it suitable for rendering translucent materials in production.
Diffuse radiation in the galaxy
  • L Heyney
  • J Greenstein
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A practical model for subsurface light transport
  • H W Jensen
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  • P Hanrahan
Jensen, H. W., Marschner, S. R., Levoy, M., and Hanrahan, P. 2001. A practical model for subsurface light transport. In Proceedings of ACM SIGGRAPH 2001, ACM Press / ACM SIGGRAPH, New York. E. Fiume, Ed., Computer Graphics Proceedings, Annual Conference Seies, ACM, 511-518.