Depth perception from stationary and moving stereoscopic three-dimensional images
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Yang, Shun-nan
Gowrisankaran, Sowjanya
Tai, Yu-Ch
Purpose: The study evaluated the accuracy of depth perception afforded by static and dynamic stereoscopic three-dimensional (S3D) images with proportional (scaled to disparity) and constant size cues. Methods: Sixty adult participants, 18 to 40 years (mean, 24.8 years), with good binocular vision participated in the study. For static S3D trials, participants were asked to indicate the depth of stationary S3D images rendered with 36, 48 and 60 pixels of crossed disparity, and with either proportional or a constant size. For dynamic S3D trials, participants were asked to indicate the time when S3D images, moving at 27, 32 and 40 pixels/sec, matched the depth of a reference image which was presented with 36, 48 and 60 pixels of crossed image disparity. Results: Results show that viewers perceived S3D images as being closer than would be predicted by the magnitude of image disparity, and correspondingly they overestimated the depth in moving S3D images. The resultant depth perception and estimate of motion speed were more accurate for conditions with proportional and larger image size, slower motion-in-depth and larger image disparity. Conclusion: These findings possibly explain why effects such as looming are over stimulating in S3D viewing. To increase the accuracy of depth perception, S3D content should match image size to its disparity level, utilize larger depth separation (without inducing excessive discomfort) and render slower motion in depth. 
 Good afternoon. Glad to have the chance to present our study. This study looked at the relationship between the amount of disparity being presented to the viewer and the accuracy of the depth reported by them. This study was funded by Intel and also we collaborated with two of their researchers. What do you want from S3D? What's the difference between S3D and 2D? Because S3D looks more real as in the real visual world, right? But in fact, if you record your experiences viewing the movie content, what you perceive is something not quite normal. You feel the pop. You feel the looming effect. It's much stronger than what you perceive in real viewing experiences. It's possible that what we are seeing as S3D does not represent the real, naturally induced, disparity based, depth perception. In this study, we are trying to verify to see if, first, S3D viewers can actually accurately estimate the depth as predicted by the disparity. We also try to look at motion in depth and to see if the viewer can use the change in disparity to estimate the motion speed and make a response based on it. Think of hitting a baseball. So if the baseball is in the real visual world versus in S3D, it would do a response to different. What we want to know is if our perception is distorted by the way we present the disparity information. So we conducted two studies. In both studies, we have the same set of subjects. We have 60 subjects. They are all young subjects. They have good binocular visual quality and stereo quality. We present the stimulus on a 55-inch passive 3D TV. The testing environment was in a dark room. The subject seated 2.4 meters away, straight ahead, away from the TV. In the first task, we present static S3D stimulus. Three variables are manipulated. The first one is the amount of closed disparity, meaning the object is supposed to be perceived as closer to the reference. The second one is the size because in a typical S3D viewing environment, we have both monocular cues and binocular cues. We want to see if the binocular cue actually is competing, collaborating, or independent of the monocular cues. So we try to manipulate the size of the stimulus. We also have one set of subject view, each constant size trial with a particular size, which is either small, medium size, or large size. So the task is very simple. We have the subject seated in the room, view the stimulus, the subject near to poor of physical indicator in the dark room, and align it to the depth he or she perceives. So here is the physical setup. So it's a top-down view. Here is the 3D screen. We have a reference object, which is the diamond. And then we have four circles with the same amount of disparity, which could be either 36, 40, or 60. Now this is the physical indicator. It's actually right underneath the stimulus. And so the subject need to pull a pulley to move the indicator from the forest distance to a distance. So what I think is the same as the depth, the circles are located. So this will be the intended depth. So the subject move the indicator until they are satisfactory in terms of just two depth of the same. Now for the constant and proportional cue conditions. So if it's a proportional cue condition. Now if we have small disparity, then the image is supposed to be further away. So we have smaller stimulus. But the constant size stimulus, the stimulus size does not change in relation to the disparity level. So here's the result. Let me explain. So here we have the perceived depth. This is centimeter away from the screen. So meaning in reference to the diamond, how far the object actually reported as a way of finding the diamond. Now we have the two types of visual cue size cues. We also manipulate the disparity level here. And so for each one sort of subject, they either see small constant cue, medium constant cue, which is a gray bar here, and large constant cue. Okay, and the top three bars are when they were seeing the proportional cue. Now as you can see, we actually use the disparate cue very well. And you can see that when there's smaller disparities, we perceive it as much closer to the screen. But if we convert this number in relation to the expected distance and calculate the proportion of error, what you are seeing is actually a constant value. At least for the small constant size and medium constant, what you are seeing is even when you present no size cues, the size is constant. The subject can still use this particular cue to infer the depth. But there's a certain amount of error. In this case, small cues is between 6 to 8% of error. Now if you are a baseball player, you know this is not good, right? Meaning, you know, it could be the difference between missing the ball and hitting out the ballpark. Now the only thing very interesting here seems to be different is when you have a large constant cue. What happens is when the disparity is smaller and then you have large image size, you actually got a much greater error compared to the same disparity but with proportional cue. And this is easy to explain because, see, you have a very large image, but you have very small disparity. So the monarchy cue is competing with binocular cue. The monarchy cue says the object is close, but the binocular cue says it's further away. And that's when you got this kind of error. In the second task, we have the same stimulus, but in this case, the false circles, it actually moved from your disparity and toward the diamond. The diamond is rendered with three level disparities, 36, 40, and 60. The circle moved from zero disparity to 80 disparities. The task for the subject is to press a button right at the moment they predict the circle is going to intersect the diamond. Now the circle is actually moved in three different speeds, pixels per second. So over here. So this is the smallest, slowest speed with constant cue. You see the image size is not changing, but it's providing increasing binocular disparity cues compared to these which is proportional and in a faster speed. And you can see the rooming effect provided by the change of image size that is absent in a constant cue condition. And so here, those are the expected time of arriving at the diamond. When you have a lower speed and you have a larger distance to travel in depth, then you got a longer latency to respond to. So here is the result. It's the time error in relation to the expected interception time. So here is many seconds. Zero indicates the response time is the same as the expected time. And so what you are seeing is what? Overestimation of speed, meaning the subject actually predicts the time arrival much earlier than the actual arrival time indicated by the disparity. So notice that if the subject view constant cues, then they perceive it as arriving much faster than it actually is. And this is paradoxical because if the cue size is not changing, it's supposed to feel like the subject is not moving in depth, right? In fact, they are estimating it as moving much faster than the one with these particles. Now if we calculate the proportion of error in relation to the expected time, so here, all we are showing here is the proportion, zero indicating the same, and here indicates 10% of error in the negative direction, meaning the estimation is overestimated. It's faster than the actual predicted time. And so what you can see is if you have small lower speeds or if you have, see, larger distance to travel, then the estimation usually is more accurate. But there is no difference between constant and proportion of cue for lower speed target. But when you have larger speed target, you see huge difference between constant cue, which has greater error compared to proportion of cue. So what those results tell us is when we are watching the 3D TV, we are actually perceiving something that is greater than what is intended, which means if you use 3D camera to shoot a real world scene, what you are doing is you are exaggerating what is actually presented in the real visual world and increase the amount of motion speed and the amount of depth. And the average we have here is between 6 to 10%. So what is happening here is there is probably a mechanism in the brain that is adding monocle cue and binocle cue together. And when you add additional binocle cues, it's adding additional depth information to it and this make you overestimate the depth. And the same can be applied to the motion perception. The implication is, yes, your eyes are deceived in 3D. Now it could be a good thing if you want to exaggerate it, you want to feel the pop, you want to feel the looming effect. But if you are applying it to a sensorimotor learning, for example, if you are using a TV to change a baseball player to hit a baseball or change an astronaut to rehearse or a task is going to perform on a spacewalk, it's not going to be optimal. You need to adjust the render disparity in order to achieve the effect you want. Thank you very much for your attention. Very interesting talk. If you have a question, please raise your hand and Stephen will bring the microphone to you. Yes, very nice. Could you calibrate the measuring device? In the sense that you are asking someone to set something in depth to appear to be at the same distance as someone else? After each trial we will set the indicator to the farthest distance. What if you had a real object and you are asking them to set a second real object to the same depth using your apparatus? Yes, we didn't do that, but I understand your concern is we can go there and verify it. Yes, it would be really simple to do. Then what do you think the effect is due to? What causes the apparent underestimation? I think in a 2D viewing environment we use the monarchy queue and other monarchy queues to infer the depth. The visual stimulation provides depth information which is encoded in the brain. When you edit binocular disparities, this information is going to be processed as well. As some part of the brain preferably MT, MST, neurons are there to add them together, but they are not tuned to receive both information in this particular task. The neuron actually sort of its activity indicates a depth that is greater than what the disparity level is intended. If this prediction is correct, then if you change the individual to do more tasks like this, the neuron should be able to gradually tune it and to make the prediction more accurate. What monarchy queues do you think are causing that? In the proportional case, the size queues right. What monarchy queues you think you know? In this case, I think size is the most predominant information. When we watch the movie, the most striking information, the coin object is moving towards us, right? That's when the size is increasing. That's the most straightforward. But you have that right in the proportional condition. It has to be something else. Maybe accommodation. Or maybe... Yes, it's possible. Yes. It's very subtle though. I don't have any knowledge about how accommodation information is encoded in terms of motion and how they are integrated by its possible explanation. Another question. Jenny Reed. Yeah, thanks. Great talk. My recollection of the literature on apparently circular cylinders and so on. Do you need a little bit slower so I can catch up with you? I'm sorry. A little more American will be hard. It's very viewing distance dependent, I believe, these effects. I think your experiments were done at one viewing distance. My recollection of the literature is that a lot of it can be explained by assuming that we don't know our own virgins. We give too much weight to, I guess, a prior to an intermediate virgins rather than our actual true virgins. Did that explain at least some of your results? Yes, that's another possible explanation, which is when you have a greater amount of disparities, your convergence angle is more likely to be drawn toward the depths. As a result, your estimation of depths could be influenced by it. Actually, on Wednesday, I'm going to present a study in which we measure the virgin angle and we manipulate the disparity level and the information should be informative. All right. I have one question. Sure. What's the only difference in your presentation from actually having real objects approaching just the accommodative queue because the screen required a constant accommodation, the disparities and the size change where all the same as if it had been a real object was the accommodation that Marty mentioned, the only discrepancy where it differed from real objects approaching? I'm a movement person, so I study eye movement. So my inclination is to say no, I think virgins is also providing information in terms of the perception of depths. Some optometrists may not agree with it because there was an accommodation is much stronger and relative depths is much useful information. But real objects would cause the same change in virgins as the stereoscopic? And we did observe that when you are tracing the object in depth, your conversion angle is increasing but not in a rate predicted by the disparity. It's much less but it's increasing. So you think a real object would produce different virgins? Yes. There would be another study in which we compare the convergence angle and see if we can predict the amount of error is related to the amount of convergence late. And so far we don't have any quantitative evidence to indicate one or not. All right. Let's thank our speaker again. Thank you.
 Good afternoon.  Glad to have the chance to present our study.  This study looked at the relationship between the amount of disparity being presented to  the viewer and the accuracy of the depth reported by them.  This study was funded by Intel and also we collaborated with two of their researchers.  What do you want from S3D?  What's the difference between S3D and 2D?  Because S3D looks more real as in the real visual world, right?  But in fact, if you record your experiences viewing the movie content, what you perceive  is something not quite normal.  You feel the pop.  You feel the looming effect.  It's much stronger than what you perceive in real viewing experiences.  It's possible that what we are seeing as S3D does not represent the real, naturally induced,  disparity based, depth perception.  In this study, we are trying to verify to see if, first, S3D viewers can actually accurately  estimate the depth as predicted by the disparity.  We also try to look at motion in depth and to see if the viewer can use the change in  disparity to estimate the motion speed and make a response based on it.  Think of hitting a baseball.  So if the baseball is in the real visual world versus in S3D, it would do a response to different.  What we want to know is if our perception is distorted by the way we present the disparity  information.  So we conducted two studies.  In both studies, we have the same set of subjects.  We have 60 subjects.  They are all young subjects.  They have good binocular visual quality and stereo quality.  We present the stimulus on a 55-inch passive 3D TV.  The testing environment was in a dark room.  The subject seated 2.4 meters away, straight ahead, away from the TV.  In the first task, we present static S3D stimulus.  Three variables are manipulated.  The first one is the amount of closed disparity, meaning the object is supposed to be perceived  as closer to the reference.  The second one is the size because in a typical S3D viewing environment, we have both monocular  cues and binocular cues.  We want to see if the binocular cue actually is competing, collaborating, or independent  of the monocular cues.  So we try to manipulate the size of the stimulus.  We also have one set of subject view, each constant size trial with a particular size,  which is either small, medium size, or large size.  So the task is very simple.  We have the subject seated in the room, view the stimulus, the subject near to poor of  physical indicator in the dark room, and align it to the depth he or she perceives.  So here is the physical setup.  So it's a top-down view.  Here is the 3D screen.  We have a reference object, which is the diamond.  And then we have four circles with the same amount of disparity, which could be either  36, 40, or 60.  Now this is the physical indicator.  It's actually right underneath the stimulus.  And so the subject need to pull a pulley to move the indicator from the forest distance  to a distance.  So what I think is the same as the depth, the circles are located.  So this will be the intended depth.  So the subject move the indicator until they are satisfactory in terms of just two depth  of the same.  Now for the constant and proportional cue conditions.  So if it's a proportional cue condition.  Now if we have small disparity, then the image is supposed to be further away.  So we have smaller stimulus.  But the constant size stimulus, the stimulus size does not change in relation to the disparity  level.  So here's the result.  Let me explain.  So here we have the perceived depth.  This is centimeter away from the screen.  So meaning in reference to the diamond, how far the object actually reported as a way  of finding the diamond.  Now we have the two types of visual cue size cues.  We also manipulate the disparity level here.  And so for each one sort of subject, they either see small constant cue, medium constant  cue, which is a gray bar here, and large constant cue.  Okay, and the top three bars are when they were seeing the proportional cue.  Now as you can see, we actually use the disparate cue very well.  And you can see that when there's smaller disparities, we perceive it as much closer  to the screen.  But if we convert this number in relation to the expected distance and calculate the  proportion of error, what you are seeing is actually a constant value.  At least for the small constant size and medium constant, what you are seeing is even when  you present no size cues, the size is constant.  The subject can still use this particular cue to infer the depth.  But there's a certain amount of error.  In this case, small cues is between 6 to 8% of error.  Now if you are a baseball player, you know this is not good, right?  Meaning, you know, it could be the difference between missing the ball and hitting out the  ballpark.  Now the only thing very interesting here seems to be different is when you have a large  constant cue.  What happens is when the disparity is smaller and then you have large image size, you actually  got a much greater error compared to the same disparity but with proportional cue.  And this is easy to explain because, see, you have a very large image, but you have  very small disparity.  So the monarchy cue is competing with binocular cue.  The monarchy cue says the object is close, but the binocular cue says it's further away.  And that's when you got this kind of error.  In the second task, we have the same stimulus, but in this case, the false circles, it actually  moved from your disparity and toward the diamond.  The diamond is rendered with three level disparities, 36, 40, and 60.  The circle moved from zero disparity to 80 disparities.  The task for the subject is to press a button right at the moment they predict the circle  is going to intersect the diamond.  Now the circle is actually moved in three different speeds, pixels per second.  So over here.  So this is the smallest, slowest speed with constant cue.  You see the image size is not changing, but it's providing increasing binocular disparity  cues compared to these which is proportional and in a faster speed.  And you can see the rooming effect provided by the change of image size that is absent  in a constant cue condition.  And so here, those are the expected time of arriving at the diamond.  When you have a lower speed and you have a larger distance to travel in depth, then you  got a longer latency to respond to.  So here is the result.  It's the time error in relation to the expected interception time.  So here is many seconds.  Zero indicates the response time is the same as the expected time.  And so what you are seeing is what?  Overestimation of speed, meaning the subject actually predicts the time arrival much earlier  than the actual arrival time indicated by the disparity.  So notice that if the subject view constant cues, then they perceive it as arriving much  faster than it actually is.  And this is paradoxical because if the cue size is not changing, it's supposed to feel  like the subject is not moving in depth, right?  In fact, they are estimating it as moving much faster than the one with these particles.  Now if we calculate the proportion of error in relation to the expected time, so here,  all we are showing here is the proportion, zero indicating the same, and here indicates  10% of error in the negative direction, meaning the estimation is overestimated.  It's faster than the actual predicted time.  And so what you can see is if you have small lower speeds or if you have, see, larger distance  to travel, then the estimation usually is more accurate.  But there is no difference between constant and proportion of cue for lower speed target.  But when you have larger speed target, you see huge difference between constant cue,  which has greater error compared to proportion of cue.  So what those results tell us is when we are watching the 3D TV, we are actually perceiving  something that is greater than what is intended, which means if you use 3D camera to shoot  a real world scene, what you are doing is you are exaggerating what is actually presented  in the real visual world and increase the amount of motion speed and the amount of depth.  And the average we have here is between 6 to 10%.  So what is happening here is there is probably a mechanism in the brain that is adding monocle  cue and binocle cue together.  And when you add additional binocle cues, it's adding additional depth information to  it and this make you overestimate the depth.  And the same can be applied to the motion perception.  The implication is, yes, your eyes are deceived in 3D.  Now it could be a good thing if you want to exaggerate it, you want to feel the pop, you  want to feel the looming effect.  But if you are applying it to a sensorimotor learning, for example, if you are using a TV  to change a baseball player to hit a baseball or change an astronaut to rehearse or a task  is going to perform on a spacewalk, it's not going to be optimal.  You need to adjust the render disparity in order to achieve the effect you want.  Thank you very much for your attention.  Very interesting talk.  If you have a question, please raise your hand and Stephen will bring the microphone  to you.  Yes, very nice.  Could you calibrate the measuring device?  In the sense that you are asking someone to set something in depth to appear to be at  the same distance as someone else?  After each trial we will set the indicator to the farthest distance.  What if you had a real object and you are asking them to set a second real object to  the same depth using your apparatus?  Yes, we didn't do that, but I understand your concern is we can go there and verify it.  Yes, it would be really simple to do.  Then what do you think the effect is due to?  What causes the apparent underestimation?  I think in a 2D viewing environment we use the monarchy queue and other monarchy queues  to infer the depth.  The visual stimulation provides depth information which is encoded in the brain.  When you edit binocular disparities, this information is going to be processed as well.  As some part of the brain preferably MT, MST, neurons are there to add them together,  but they are not tuned to receive both information in this particular task.  The neuron actually sort of its activity indicates a depth that is greater than what the disparity  level is intended.  If this prediction is correct, then if you change the individual to do more tasks like  this, the neuron should be able to gradually tune it and to make the prediction more accurate.  What monarchy queues do you think are causing that?  In the proportional case, the size queues right.  What monarchy queues you think you know?  In this case, I think size is the most predominant information.  When we watch the movie, the most striking information, the coin object is moving towards  us, right?  That's when the size is increasing.  That's the most straightforward.  But you have that right in the proportional condition.  It has to be something else.  Maybe accommodation.  Or maybe...  Yes, it's possible.  Yes.  It's very subtle though.  I don't have any knowledge about how accommodation information is encoded in terms of motion  and how they are integrated by its possible explanation.  Another question.  Jenny Reed.  Yeah, thanks.  Great talk.  My recollection of the literature on apparently circular cylinders and so on.  Do you need a little bit slower so I can catch up with you?  I'm sorry.  A little more American will be hard.  It's very viewing distance dependent, I believe, these effects.  I think your experiments were done at one viewing distance.  My recollection of the literature is that a lot of it can be explained by assuming that  we don't know our own virgins.  We give too much weight to, I guess, a prior to an intermediate virgins rather than our  actual true virgins.  Did that explain at least some of your results?  Yes, that's another possible explanation, which is when you have a greater amount of  disparities, your convergence angle is more likely to be drawn toward the depths.  As a result, your estimation of depths could be influenced by it.  Actually, on Wednesday, I'm going to present a study in which we measure the virgin angle  and we manipulate the disparity level and the information should be informative.  All right.  I have one question.  Sure.  What's the only difference in your presentation from actually having real objects approaching  just the accommodative queue because the screen required a constant accommodation, the disparities  and the size change where all the same as if it had been a real object was the accommodation  that Marty mentioned, the only discrepancy where it differed from real objects approaching?  I'm a movement person, so I study eye movement.  So my inclination is to say no, I think virgins is also providing information in terms of  the perception of depths.  Some optometrists may not agree with it because there was an accommodation is much stronger  and relative depths is much useful information.  But real objects would cause the same change in virgins as the stereoscopic?  And we did observe that when you are tracing the object in depth, your conversion angle  is increasing but not in a rate predicted by the disparity.  It's much less but it's increasing.  So you think a real object would produce different virgins?  Yes.  There would be another study in which we compare the convergence angle and see if we can predict  the amount of error is related to the amount of convergence late.  And so far we don't have any quantitative evidence to indicate one or not.  All right.  Let's thank our speaker again.  Thank you.
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