FOVO: A new 3D rendering technique based on human vision(gamasutra.com)
gamasutra.com
FOVO: A new 3D rendering technique based on human vision
https://www.gamasutra.com/blogs/RobertPepperell/20200527/363615/FOVO_A_new_3D_rendering_technique_based_on_human_vision.php
87 comments
Most comments here point out that this may be just a fish-eye camera model with some fancy sales language around it. This was my initial thought as well, however, it should be pointed out that changing from linear to fish-eye doesn't cause any changes in scene occlusion. It's just moving pixels on a 2D plane. The technique described however does change occlusion.
Based on the examples on the page, here's how I think it actually works: The projection center is not a single point in space but rather varies along the optical axis depending on the position of the 3D vertex as seen from the camera. More specifically: For each vertex a preliminary linear projection is computed to get an XY position, then the projection is recomputed using the preliminary XY position, where things near the center are re-projected using a projection center that is moved forward along the opt. axis and things near the borders/corners of the image have it moved backward. On top of this then probably a standard fish-eye.
That said, I'm not sure this whole projection trickery is really worth it, given that it comes with its own issues (your scene will appear to warp when you turn the camera) and given that you can probably get 90% of the value with one of the more standard fish-eye models.
Based on the examples on the page, here's how I think it actually works: The projection center is not a single point in space but rather varies along the optical axis depending on the position of the 3D vertex as seen from the camera. More specifically: For each vertex a preliminary linear projection is computed to get an XY position, then the projection is recomputed using the preliminary XY position, where things near the center are re-projected using a projection center that is moved forward along the opt. axis and things near the borders/corners of the image have it moved backward. On top of this then probably a standard fish-eye.
That said, I'm not sure this whole projection trickery is really worth it, given that it comes with its own issues (your scene will appear to warp when you turn the camera) and given that you can probably get 90% of the value with one of the more standard fish-eye models.
> That said, I'm not sure this whole projection trickery is really worth it
I think it might be. Look at the first GIF in the article, the one showing the room. Try to imagine yourself standing in that room. Doesn't the FOVO-corrected picture look pretty much like what you'd see with your own eyes if you were the camera? It definitely looks like that for me.
Using standard projections for rendering, there's no FOV value that would produce this effect - at a typical videogame FOV of 90°, you see only a fragment of what your eyes would perceive, and as you increase FOV, distortions become really bad.
I went into more details on this here: https://news.ycombinator.com/item?id=26805010.
I think it might be. Look at the first GIF in the article, the one showing the room. Try to imagine yourself standing in that room. Doesn't the FOVO-corrected picture look pretty much like what you'd see with your own eyes if you were the camera? It definitely looks like that for me.
Using standard projections for rendering, there's no FOV value that would produce this effect - at a typical videogame FOV of 90°, you see only a fragment of what your eyes would perceive, and as you increase FOV, distortions become really bad.
I went into more details on this here: https://news.ycombinator.com/item?id=26805010.
> Doesn't the FOVO-corrected picture look pretty much like what you'd see with your own eyes if you were the camera?
No, because the picture doesn’t occupy a 160-degree FOV.
Is it a more pleasing way of collapsing the wider FOV into a narrower one? Yes. But what is even more pleasing is to use a display system which occupies the FOV being simulated.
No, because the picture doesn’t occupy a 160-degree FOV.
Is it a more pleasing way of collapsing the wider FOV into a narrower one? Yes. But what is even more pleasing is to use a display system which occupies the FOV being simulated.
> Look at the first GIF in the article, the one showing the room.
How do you know which of those two pictures is the FOVO one?
How do you know which of those two pictures is the FOVO one?
Because I've played enough FPS games as a kid, and played enough with FOV parameter in their debug consoles to know how 160° FOV looks like on a render :). The FOVO picture is the one that looks normal; the stretchy one is a traditional perspective projection.
(As for why I played with FOV command? Curiosity, plus if you bound a key on your keyboard to set FOV to 30-40 on keypress and restore it to normal on release, you got free sniper scope on every weapon :).)
(As for why I played with FOV command? Curiosity, plus if you bound a key on your keyboard to set FOV to 30-40 on keypress and restore it to normal on release, you got free sniper scope on every weapon :).)
Surely your're joking... Right ?
It is interesting that they chose to use a first person shooter as an example screenshot since gameplay would be broken by visual changes in occlusion.
> your scene will appear to warp when you turn the camera
But only near the edges, I suppose?
Human vision is not very detailed near the edges of the field of view, so perhaps a reproduction of that vision shouldn't be either?
But only near the edges, I suppose?
Human vision is not very detailed near the edges of the field of view, so perhaps a reproduction of that vision shouldn't be either?
Your scene will appear to warp when you turn the camera anyway, unless you put your eye at the one point in space where it matches the projection. Few people put their eyes so close to the screen.
This looks an awful lot like a variation of the Panini projection described in this paper [1], which I implemented for RenderMan [2].
The tell-tale sign to me is that the perspective lines that converge towards the vanishing point remain straight, while the other lines bow. You can see it pretty clearly in the last two pictures on their archvis page [3].
[1] http://tksharpless.net/vedutismo/Pannini/panini.pdf
[2] https://rmanwiki.pixar.com/display/REN23/PxrPanini
[3] https://www.fovotec.com/architectural
The tell-tale sign to me is that the perspective lines that converge towards the vanishing point remain straight, while the other lines bow. You can see it pretty clearly in the last two pictures on their archvis page [3].
[1] http://tksharpless.net/vedutismo/Pannini/panini.pdf
[2] https://rmanwiki.pixar.com/display/REN23/PxrPanini
[3] https://www.fovotec.com/architectural
> which I implemented for RenderMan
Cool, you work at Pixar? I've been working on a small hobby renderer [1] for a few years, and RenderMan has been a big inspiration!
[1] https://github.com/vkoskiv/c-ray
Cool, you work at Pixar? I've been working on a small hobby renderer [1] for a few years, and RenderMan has been a big inspiration!
[1] https://github.com/vkoskiv/c-ray
I used to until recently, but decided I was ready to try something new.
Glad you found RenderMan an inspiration. I had too when I was working on my own hobby renderer before joining Pixar! Yours looks very nice.
Glad you found RenderMan an inspiration. I had too when I was working on my own hobby renderer before joining Pixar! Yours looks very nice.
It seems like it's supposed to be "Pannini", after a painter (https://en.wikipedia.org/wiki/Image_stitching#Pannini), and that's what the original paper says.
But when the lead author spells it both ways in the same URL, and many software implementations use "Panini", it gets a bit confusing!
But when the lead author spells it both ways in the same URL, and many software implementations use "Panini", it gets a bit confusing!
That's because historically it has been spelled both ways. I believe that even the artist himself was inconsistent. However, I'd read somewhere that he tended to sign his paintings with the single-n spelling, so that's what I went with for the plugin name.
He was probably very hungry, never think with your stomach!
Pannini Projection doesn't change occlusions though as described in the article.
My BS-o-meter went red when I read this:
> This demonstrates that the FOVO process is not simply ‘warping’ a 2D render in screen space but is applying nonlinear transformations to the entire 3D geometry.
This seems to conflate an implementation detail with what what is actually happening here.
It doesn't matter if what you do is a some non-linear 2D projection applied to 3D vertices or 2D pixels (or 2D projected vertices). The 'light rays' we concern ourselves with don't bend around. I.e. this is just another non-linear projection.
All this sounds to me like fluff to bolster a business/patent application for what is essentially a fancy fisheye transform.
> This demonstrates that the FOVO process is not simply ‘warping’ a 2D render in screen space but is applying nonlinear transformations to the entire 3D geometry.
This seems to conflate an implementation detail with what what is actually happening here.
It doesn't matter if what you do is a some non-linear 2D projection applied to 3D vertices or 2D pixels (or 2D projected vertices). The 'light rays' we concern ourselves with don't bend around. I.e. this is just another non-linear projection.
All this sounds to me like fluff to bolster a business/patent application for what is essentially a fancy fisheye transform.
I think the point is not to talk about how it's implemented, but to point out that it's a more complex change, one that cannot be achieved by first performing a traditional render and then transforming the resulting 2d image. You actually need to project differently.
And how should that be more natural, as they claim?
A human eye is just a lens. With very well studied geometry.
The brain can do amazing feats, e.g. make bend/warped parts of images it sees straight again. A friend of mine who's 20 year old LASIK is coming apart, unfortunately, experienced it first hand recently.
But it can't see behind objects.
So if the occlusion changes as, they claim, there are two possibilities:
1. The camera was moved.
2. The scene geometry was distorted. That is possible in a computer but not in reality.
I.e. the result can never be more natural than what you can already model with a 2D fisheye projection based on human eye geometry.
A human eye is just a lens. With very well studied geometry.
The brain can do amazing feats, e.g. make bend/warped parts of images it sees straight again. A friend of mine who's 20 year old LASIK is coming apart, unfortunately, experienced it first hand recently.
But it can't see behind objects.
So if the occlusion changes as, they claim, there are two possibilities:
1. The camera was moved.
2. The scene geometry was distorted. That is possible in a computer but not in reality.
I.e. the result can never be more natural than what you can already model with a 2D fisheye projection based on human eye geometry.
Things can look and feel more natural without actually technically being more natural.
Yeah, it’s BS. There are a bunch of fisheye transforms, including Stereographic, equidistant, and equisolid angle.
"emulating human vision" is a distraction here. If you have a flat 22 inch 16:10 display that you view from a 1m distance, dead centre, then the most natural way to render is to use linear projection with a FOV of ~26 degrees, which is absolutely tiny. But this matches the same view as if you were looking out a window the same size of your display.
Since such a tiny FOV is impractical, any rendering that wants to cover more FOV has to distort the view some way. There will be regions on the screen with more or less distortions, this becomes a trade-off. Arguably linear projection with a large FOV does a poor job at this.
I also wonder if there are projection techniques that specifically optimize for curved screens.
Since such a tiny FOV is impractical, any rendering that wants to cover more FOV has to distort the view some way. There will be regions on the screen with more or less distortions, this becomes a trade-off. Arguably linear projection with a large FOV does a poor job at this.
I also wonder if there are projection techniques that specifically optimize for curved screens.
Pretending that the screen i just a window into the real world might be the most technically correct way to do it, but that doesn't mean it's the one we will experience as most real or most engaging.
> "emulating human vision" is a distraction here.
Is it though? I read your comment first and agreed with what you wrote, but then I saw the GIF at the top of the article and instantly got what they're doing. They're solving what I call the problem of "taking a photo of what you see".
(I don't know what the professional vocabulary is for this, so please excuse the long-winded explanation.)
Here's the scenario: when I sit on the chair and look straight forward without moving, I can see a certain area of the room in a certain way. If I take out my phone, place it where my eyes are and take a photo, the resulting image looks nothing like what I just saw from that position. The biggest difference is, it captures much less of the field of view, but also the angles look wrong.
I also repeated this "experiment" virtually. The other day, I was doing some interior design in Blender, and created a room and a corridor using real dimensions. I tried, out of curiosity, to render a view from the perspective of myself (using my eye height). I explored camera configurations for a good hour, and couldn't find anything that would render the image corresponding to how I would perceive that space in real life. Again, the main problem was, either the field of view was too small, or angles were too badly distorted.
Of course a flat image of a scene, viewed from distance, is not the same as seeing the scene itself. Both my phone and Blender are correct. Perhaps they reproduce exactly what I'd see if my phone and screen were windows to the scene. But that's not what I'm after, that's not how my brain tries to interpret these images[0]! Instead, my mind tries to comprehend the image as if my eyes were the camera. It does some weird and hard to describe mapping[1], trying to put itself in the place of the camera, and immediately notices that the image doesn't look like what eyes would perceive.
When I saw that GIF showing a room rendered with hFOV=160°, the FOVO variant immediately clicked. My brain took that image, did its weird mapping, and concluded that this is exactly how the room would look like to me, if I was standing where the camera is!
So in my books, whatever they're doing, it's a big step in the right direction. These corrected renders feel like what I'd see if I were the camera. For games, this could improve immersion when playing on a flat screen. Outside of games, such transform on a photo would help people communicate their perception of the things they photograph.
--
[0] - In this case. I have no problem seeing a picture as a picture, but I can also try to see it as recording of perception. It's a choice I can make. I assume that's pretty standard and everyone can do this if the image looks close enough to first-person view.
[1] - The best description I can come up with - and mind you, I'm going off my feelings and introspection - is as follows. Assume a simplified model of sight as made of 3 layers. In layer 1, you have raw input signals from the eyes, which give you two tiny images. Layer 2 expands these images into an unified, much larger image, correcting for saccades and filling in blanks via memory and inference. Then layer 3 tries to understand the image.
What my brain seems to do, when I'm trying to comprehend a first person perspective render or a photo, is identifying that artificial picture, isolating it and spawning a subprocess running just layer 3, feeding that artificial picture as its input, as if it came from the layer 2.
Is it though? I read your comment first and agreed with what you wrote, but then I saw the GIF at the top of the article and instantly got what they're doing. They're solving what I call the problem of "taking a photo of what you see".
(I don't know what the professional vocabulary is for this, so please excuse the long-winded explanation.)
Here's the scenario: when I sit on the chair and look straight forward without moving, I can see a certain area of the room in a certain way. If I take out my phone, place it where my eyes are and take a photo, the resulting image looks nothing like what I just saw from that position. The biggest difference is, it captures much less of the field of view, but also the angles look wrong.
I also repeated this "experiment" virtually. The other day, I was doing some interior design in Blender, and created a room and a corridor using real dimensions. I tried, out of curiosity, to render a view from the perspective of myself (using my eye height). I explored camera configurations for a good hour, and couldn't find anything that would render the image corresponding to how I would perceive that space in real life. Again, the main problem was, either the field of view was too small, or angles were too badly distorted.
Of course a flat image of a scene, viewed from distance, is not the same as seeing the scene itself. Both my phone and Blender are correct. Perhaps they reproduce exactly what I'd see if my phone and screen were windows to the scene. But that's not what I'm after, that's not how my brain tries to interpret these images[0]! Instead, my mind tries to comprehend the image as if my eyes were the camera. It does some weird and hard to describe mapping[1], trying to put itself in the place of the camera, and immediately notices that the image doesn't look like what eyes would perceive.
When I saw that GIF showing a room rendered with hFOV=160°, the FOVO variant immediately clicked. My brain took that image, did its weird mapping, and concluded that this is exactly how the room would look like to me, if I was standing where the camera is!
So in my books, whatever they're doing, it's a big step in the right direction. These corrected renders feel like what I'd see if I were the camera. For games, this could improve immersion when playing on a flat screen. Outside of games, such transform on a photo would help people communicate their perception of the things they photograph.
--
[0] - In this case. I have no problem seeing a picture as a picture, but I can also try to see it as recording of perception. It's a choice I can make. I assume that's pretty standard and everyone can do this if the image looks close enough to first-person view.
[1] - The best description I can come up with - and mind you, I'm going off my feelings and introspection - is as follows. Assume a simplified model of sight as made of 3 layers. In layer 1, you have raw input signals from the eyes, which give you two tiny images. Layer 2 expands these images into an unified, much larger image, correcting for saccades and filling in blanks via memory and inference. Then layer 3 tries to understand the image.
What my brain seems to do, when I'm trying to comprehend a first person perspective render or a photo, is identifying that artificial picture, isolating it and spawning a subprocess running just layer 3, feeding that artificial picture as its input, as if it came from the layer 2.
> When I saw that GIF showing a room rendered with hFOV=160°
Are we looking at the same picture? The first render in the article? The picture is a flat projection about 10cm wide on my screen, so it's displayed on a tiny FoV. Looking at a picture of 160deg render, stretched over 10(?) degrees of my field of view just looks like an extreme fish eye? The article suggests it's "natural" and it may well be if I'd see in it VR or at least a huge display... but maybe I'm not getting it?
And what are these "occlusion" path the article talks about? How does the edge of the building become visible above the head of the robot in any projection, unless the eye moves? Light either a) reaches the aperture from the edge of the roof, or b) it doesn't reach the aperture because it's occluded by the robot. Anything else would imply some non-euclidean light transport here like those black hole renderings...
The robot gif looks like it does something really strange wrt occlusion. But what? The caption mentions occlusion paths and nonlinearity, but neither of those terms are found in the rest of the article!
Are we looking at the same picture? The first render in the article? The picture is a flat projection about 10cm wide on my screen, so it's displayed on a tiny FoV. Looking at a picture of 160deg render, stretched over 10(?) degrees of my field of view just looks like an extreme fish eye? The article suggests it's "natural" and it may well be if I'd see in it VR or at least a huge display... but maybe I'm not getting it?
And what are these "occlusion" path the article talks about? How does the edge of the building become visible above the head of the robot in any projection, unless the eye moves? Light either a) reaches the aperture from the edge of the roof, or b) it doesn't reach the aperture because it's occluded by the robot. Anything else would imply some non-euclidean light transport here like those black hole renderings...
The robot gif looks like it does something really strange wrt occlusion. But what? The caption mentions occlusion paths and nonlinearity, but neither of those terms are found in the rest of the article!
> The article suggests it's "natural" and it may well be if I'd see in it VR or at least a huge display... but maybe I'm not getting it?
Right-click on the GIF and open it in a new tab, and zoom it up a bit if it helps. Then try to look at it as if you were playing a video game, or imagining yourself being in the room.
The linear projection image, the "one that looks like the space is being sucked into a vortex" is what you'd get in a traditional game if you set it to render the same visual field human eyes see, instead of 90° that's typical for first-person shooters. The other one, the one that looks slightly bendy around the edges but otherwise OK, is much closer to what you'd see if you were standing where the camera is.
> The picture is a flat projection about 10cm wide on my screen, so it's displayed on a tiny FoV.
That's not the perspective I'm talking about. I'm talking about the other one, in which you look at the picture on the screen, but see it as if the picture was your entire field of view. I'm not sure how to explain it better than I tried in my previous comment, so let me put it this way:
Look away from the screen, down the length of the room you're currently in. Absorb the space you see, without moving your head. Try to do a mental "screenshot". Imagine downloading it from your brain and viewing it on your computer screen. It would look completely different from a real photo taken from where you're sitting. That FOVO render seems to be close to what the "mental screenshot" would look like on a computer screen, at least in my case. And it's the first time I saw anyone doing it.
Right-click on the GIF and open it in a new tab, and zoom it up a bit if it helps. Then try to look at it as if you were playing a video game, or imagining yourself being in the room.
The linear projection image, the "one that looks like the space is being sucked into a vortex" is what you'd get in a traditional game if you set it to render the same visual field human eyes see, instead of 90° that's typical for first-person shooters. The other one, the one that looks slightly bendy around the edges but otherwise OK, is much closer to what you'd see if you were standing where the camera is.
> The picture is a flat projection about 10cm wide on my screen, so it's displayed on a tiny FoV.
That's not the perspective I'm talking about. I'm talking about the other one, in which you look at the picture on the screen, but see it as if the picture was your entire field of view. I'm not sure how to explain it better than I tried in my previous comment, so let me put it this way:
Look away from the screen, down the length of the room you're currently in. Absorb the space you see, without moving your head. Try to do a mental "screenshot". Imagine downloading it from your brain and viewing it on your computer screen. It would look completely different from a real photo taken from where you're sitting. That FOVO render seems to be close to what the "mental screenshot" would look like on a computer screen, at least in my case. And it's the first time I saw anyone doing it.
> Right-click on the GIF and open it in a new tab, and zoom it up a bit if it helps.
That makes more sense (although I'd probably never get used to that in a flat projection (screen) type game, no matter how hard I tried to immerse in it. The extreme distortions of things round details being very oval etc seems like they would break immersion very quickly, but might work in VR.
I still don't understand wth is going on though with the occlusion.
That makes more sense (although I'd probably never get used to that in a flat projection (screen) type game, no matter how hard I tried to immerse in it. The extreme distortions of things round details being very oval etc seems like they would break immersion very quickly, but might work in VR.
I still don't understand wth is going on though with the occlusion.
It seems like you're actually a target audience with your Blender example:
https://www.fovotec.com/architectural
Indeed, we're remodeling the house, and our architect is going to give us some 3D renderings, and I know it's going to look nothing like when we're actually there. Maybe should propose this to him...
https://www.fovotec.com/architectural
Indeed, we're remodeling the house, and our architect is going to give us some 3D renderings, and I know it's going to look nothing like when we're actually there. Maybe should propose this to him...
Isn't a consumer VR headset a way better solution to that desire?
Or more importantly, don't have your architect render your house with a 170 degree FOV. This wasn't an issue when my dad did this in early 2000s with his house. It was in 3D modelling software and you could look all around it and modify it.
Strangely, especially in the motion video, the image looks a lot more... 3D even though it's a 2D image. It's quite interesting!
Hmm, sounds like a lot of sales talk around what seems to be a "nice fisheye effect".
The first gif of that room in the article shows exactly the problem: rasterizing works by transforming a bunch of triangles from world space to view space.
This works with rectilinear projection because each triangle can be transformed into a different shaped triangle, based on the perspective of the camera. You can't transform a triangle into a "bendy" triangle. And the screenshots show "bendy" lines.
So, if you want a "fisheye" effect in rasterization, you first need to render a rectilinear image, and then distort it, leaving you with a blurry center due to a lack of resolution.
The first gif of that room in the article shows exactly the problem: rasterizing works by transforming a bunch of triangles from world space to view space.
This works with rectilinear projection because each triangle can be transformed into a different shaped triangle, based on the perspective of the camera. You can't transform a triangle into a "bendy" triangle. And the screenshots show "bendy" lines.
So, if you want a "fisheye" effect in rasterization, you first need to render a rectilinear image, and then distort it, leaving you with a blurry center due to a lack of resolution.
I only skimmed the article, but knowing previous solutions, there were attempts to use real subdivision and displacement of the original geometry.
The article does mention this line:
> These subtle adjustments of the 3D space are being applied volumetrically, as can be seen from the way the occlusion paths in the scene are changing. This demonstrates that the FOVO process is not simply ‘warping’ a 2D render in screen space but is applying nonlinear transformations to the entire 3D geometry.
I'm not sure how to interpret this in real terms, but it does seem to suggest at least it might be more than a simple 2d transform
The article does mention this line:
> These subtle adjustments of the 3D space are being applied volumetrically, as can be seen from the way the occlusion paths in the scene are changing. This demonstrates that the FOVO process is not simply ‘warping’ a 2D render in screen space but is applying nonlinear transformations to the entire 3D geometry.
I'm not sure how to interpret this in real terms, but it does seem to suggest at least it might be more than a simple 2d transform
Tesselation is sometimes pitched as an alternative, but it's no silver bullet.
Regarding the "volumetric" bit, I think it just means that the camera is moved forward just a little bit.
Regarding the "volumetric" bit, I think it just means that the camera is moved forward just a little bit.
I mean, interesting article and all, but I wish they would have used labeled side by side comparison for the renders instead of a GIF. It's tedious to figure out which is which like this.
I disagree. Switching between two images in the same space is one of the best ways to perceive differences. Another good way is with a spider, which is used at https://www.fovotec.com/architectural
https://www.fovotec.com/architectural has a more useful demo than the basic GIFs on that page.
Thank you!
I was struggling to understand which of the pictures in those GIFs is standard and which is FOVO. This demo is super clear.
> The result is FOVO, a new method of rendering 3D space that emulates the human visual system, not cameras.
I don't buy this part. Sure, the human visual system does all kinds of processing that causes us to perceive our surroundings differently than we do a flat image on a screen. But the input to the visual system is still an image on your retina, which obeys the same laws as camera optics.
If you ignore depth of field (which the authors don't seem to be concerned with) then the human eye behaves essentially like a pinhole camera. No matter what happens behind the pinhole, all of the light rays entering the eye must pass through the pinhole from the environment, traveling in straight lines. For any given viewpoint, the relationships of which objects are occluded by which other objects will be exactly the same for an eye as for a camera. But the authors specifically point out that their algorithm doesn't preserve these relationships.
Of course, computer graphics is as much an art as a science. If deviating from the realistic model turns out to give aesthetically pleasing results, then by all means go for it. But the reason it's better wouldn't have anything to do with more closely mimicking what the human eye actually perceives.
In any case, I would question the aesthetic benefits. To my eye, the algorithm seems to distort relative shapes and sizes of objects in a weird way. It looks great in screenshots, but when moving through a scene[1], it creates a subtly unsettling "space-warping" effect.
I also think the comparisons in the article are a little disingenuous, because everyone knows that linear perspective projections with wide FOV look horrible. I'd like to see a comparison against something else, like a stereographic or fisheye projection, which would be both more physically realistic and more efficient to render.
[1]: https://www.fovotec.com/architectural
I don't buy this part. Sure, the human visual system does all kinds of processing that causes us to perceive our surroundings differently than we do a flat image on a screen. But the input to the visual system is still an image on your retina, which obeys the same laws as camera optics.
If you ignore depth of field (which the authors don't seem to be concerned with) then the human eye behaves essentially like a pinhole camera. No matter what happens behind the pinhole, all of the light rays entering the eye must pass through the pinhole from the environment, traveling in straight lines. For any given viewpoint, the relationships of which objects are occluded by which other objects will be exactly the same for an eye as for a camera. But the authors specifically point out that their algorithm doesn't preserve these relationships.
Of course, computer graphics is as much an art as a science. If deviating from the realistic model turns out to give aesthetically pleasing results, then by all means go for it. But the reason it's better wouldn't have anything to do with more closely mimicking what the human eye actually perceives.
In any case, I would question the aesthetic benefits. To my eye, the algorithm seems to distort relative shapes and sizes of objects in a weird way. It looks great in screenshots, but when moving through a scene[1], it creates a subtly unsettling "space-warping" effect.
I also think the comparisons in the article are a little disingenuous, because everyone knows that linear perspective projections with wide FOV look horrible. I'd like to see a comparison against something else, like a stereographic or fisheye projection, which would be both more physically realistic and more efficient to render.
[1]: https://www.fovotec.com/architectural
> No matter what happens behind the pinhole, all of the light rays entering the eye must pass through the pinhole from the environment, traveling in straight lines. For any given viewpoint, the relationships of which objects are occluded by which other objects will be exactly the same for an eye as for a camera.
This is too simplified.
1. The light falls onto a curved surface of the retina, not a flat screen behind it, but more importantly
2. Our brains interpret the light that falls and creates the experience of a 3D space in front of us.
If you actually simply flattened out the retina and mapped out, point to point, the light that falls, it would look a bit like their linear perspective example. The center would be clear, and the edges would be horribly stretched. And yet that's nothing like the way we perceive our vision. Looking at the image on the back of our retina rolled out like a painting, we wouldn't recognize it at all as what we see.
This is attempting to create a flat image that looks like what we see in front of us when we're standing in a space.
This is too simplified.
1. The light falls onto a curved surface of the retina, not a flat screen behind it, but more importantly
2. Our brains interpret the light that falls and creates the experience of a 3D space in front of us.
If you actually simply flattened out the retina and mapped out, point to point, the light that falls, it would look a bit like their linear perspective example. The center would be clear, and the edges would be horribly stretched. And yet that's nothing like the way we perceive our vision. Looking at the image on the back of our retina rolled out like a painting, we wouldn't recognize it at all as what we see.
This is attempting to create a flat image that looks like what we see in front of us when we're standing in a space.
I agree that my explanation is simplified, but not in a way that affects my point. Maybe I didn't explain it well, so I'll try again.
Imagine you're trying to draw a flat map of the curved surface of the earth. There are many possible ways you could do it, with differing tradeoffs between preserving sizes, preserving angles, and so on. But any faithful map should respect basic geometric constraints. If your map shows the Statue of Liberty as being in Manhattan, it's quite simply wrong.
Likewise, we can think about which distortions can possibly arise from a physical model of the eye. Regardless how the curved surface of the retina is shaped, light travels in straight lines until it hits the pupil. That means the geometric relationships between distant and nearby points -- that is, which points "line up" with others -- are constrained to match those straight lines. FOVO does not obey these constraints (as mentioned in their explanation, and as demonstrated in their examples).
> 1. The light falls onto a curved surface of the retina, not a flat screen behind it, but more importantly
I didn't say anything about how the retina is shaped, because it doesn't matter. If a distant point A, a nearby point B, and the viewpoint V are all collinear, then A can't be visible because it's obscured by B. The exact details of where B appears in the resulting image would depend on the shape of the retina, true. But wherever B is, A must be mapped to the same point, which means A must be occluded unless the light rays are traveling in curved paths outside the eye.
> 2. Our brains interpret the light that falls and creates the experience of a 3D space in front of us.
Agreed. But your brain can only perceive things that are based on some transformation of the image that is projected on the retina. If it creates the "experience" of seeing an object whose light is physically obstructed, then it's not a perception, it's a hallucination.
> If you actually simply flattened out the retina and mapped out, point to point, the light that falls, it would look a bit like their linear perspective example.
Not really; it depends on how you do the mapping. Their example shows what an image projected onto an extremely wide flat retina would look like. This is mathematically equivalent to a "gnomonic" map projection, which hugely distorts shapes and distances as you get farther from the center. You could use any other projection (defined as a function mapping between view directions and image coordinates) without breaking the geometric perspective relationships, and without needing to modify the 3D geometry of the scene at rendering time.
Imagine you're trying to draw a flat map of the curved surface of the earth. There are many possible ways you could do it, with differing tradeoffs between preserving sizes, preserving angles, and so on. But any faithful map should respect basic geometric constraints. If your map shows the Statue of Liberty as being in Manhattan, it's quite simply wrong.
Likewise, we can think about which distortions can possibly arise from a physical model of the eye. Regardless how the curved surface of the retina is shaped, light travels in straight lines until it hits the pupil. That means the geometric relationships between distant and nearby points -- that is, which points "line up" with others -- are constrained to match those straight lines. FOVO does not obey these constraints (as mentioned in their explanation, and as demonstrated in their examples).
> 1. The light falls onto a curved surface of the retina, not a flat screen behind it, but more importantly
I didn't say anything about how the retina is shaped, because it doesn't matter. If a distant point A, a nearby point B, and the viewpoint V are all collinear, then A can't be visible because it's obscured by B. The exact details of where B appears in the resulting image would depend on the shape of the retina, true. But wherever B is, A must be mapped to the same point, which means A must be occluded unless the light rays are traveling in curved paths outside the eye.
> 2. Our brains interpret the light that falls and creates the experience of a 3D space in front of us.
Agreed. But your brain can only perceive things that are based on some transformation of the image that is projected on the retina. If it creates the "experience" of seeing an object whose light is physically obstructed, then it's not a perception, it's a hallucination.
> If you actually simply flattened out the retina and mapped out, point to point, the light that falls, it would look a bit like their linear perspective example.
Not really; it depends on how you do the mapping. Their example shows what an image projected onto an extremely wide flat retina would look like. This is mathematically equivalent to a "gnomonic" map projection, which hugely distorts shapes and distances as you get farther from the center. You could use any other projection (defined as a function mapping between view directions and image coordinates) without breaking the geometric perspective relationships, and without needing to modify the 3D geometry of the scene at rendering time.
I do see your point better now, thank you.
You're right, of course. Our visual perception of the space can't cause things to become occluded when they weren't before, and vice-versa.
So maybe that ends up being a side-effect of an implementation detail, and in their zeal to explain how, to achieve a realistic-feeling FOV, they had to change how light bends through space, they ended up harping too much on this side-effect as a way to describe what they meant.
I think the question is simply whether a better transform exists using just the 2D image. If one exists that feels like a not-too-distorted wide-angle view like our natural vision, then people should be using that. If this turns out to be the best way to achieve that currently, and it has a slight side-effect of a change in occlusion, then this seems like a good thing.
You're right, of course. Our visual perception of the space can't cause things to become occluded when they weren't before, and vice-versa.
So maybe that ends up being a side-effect of an implementation detail, and in their zeal to explain how, to achieve a realistic-feeling FOV, they had to change how light bends through space, they ended up harping too much on this side-effect as a way to describe what they meant.
I think the question is simply whether a better transform exists using just the 2D image. If one exists that feels like a not-too-distorted wide-angle view like our natural vision, then people should be using that. If this turns out to be the best way to achieve that currently, and it has a slight side-effect of a change in occlusion, then this seems like a good thing.
>But the input to the visual system is still an image on your retina, which obeys the same laws as camera optics.
The thing that makes the difference is that the resolution of the retina drops as you move off the center. Which I suppose is what is being simulated here. Or at least it could be efficiently simulated that way - like foveated rendering, only the fovea is kept at the center and the rest of the image is kept with the pixels smashed closer together instead of interpolated.
The thing that makes the difference is that the resolution of the retina drops as you move off the center. Which I suppose is what is being simulated here. Or at least it could be efficiently simulated that way - like foveated rendering, only the fovea is kept at the center and the rest of the image is kept with the pixels smashed closer together instead of interpolated.
If that were the only difference, then FOVO would be equivalent to a 2D image warp. But the authors claim they're doing something different, and their examples demonstrate that they're actually moving objects relative to each other in 3D space. Despite the name, the system seems to have nothing to do with foveated rendering.
How different is this from a subtle fisheye lens effect? I feel like I saw such a thing (Fisheye projection) rendered in the original Quake years ago and it looks very similar to the outputs in the article
edit: http://strlen.com/gfxengine/fisheyequake/
edit: http://strlen.com/gfxengine/fisheyequake/
It looks nothing like it. FOVO looks perfectly natural to me. Fish eye doesn't. I'm baffled by the comments on this post. This is not a fisheye effect at all. It's visually clear to me.
How strong of a fisheye effect are you talking about? The quake example has a much stronger effect than I'm talking about. But years ago I bought a cheap "fisheye" (it was really just a super wide angle) lens and it produced an effect very similar to what this paper and Fisheye Quake shows
Unfortunately this article doesn't explain what FOVO actually does differently, just that it's not normal linear perspective.
I'm guessing what it's doing is the effect you get from when you take a panorama photo with your phone, so each column in the image is rendered as if in the center of a lens rather than at a side -- thus producing the curved lines we associate with panoramas, and that seem to be in these photos.
But that's not new, so what did they supposedly invent? Is it actually some new kind of projection that isn't the same as how panorama photos work? Or is it just that, but nobody had built it for 3D rendering software before and they did? Or people had, but they made it work in real-time?
I'm guessing what it's doing is the effect you get from when you take a panorama photo with your phone, so each column in the image is rendered as if in the center of a lens rather than at a side -- thus producing the curved lines we associate with panoramas, and that seem to be in these photos.
But that's not new, so what did they supposedly invent? Is it actually some new kind of projection that isn't the same as how panorama photos work? Or is it just that, but nobody had built it for 3D rendering software before and they did? Or people had, but they made it work in real-time?
It looks like it's essentially just a tessellation + vertex shader run on the entire scene. Say you have a lamp-post mesh in your scene. Rather than drawing polygons with straight lines (normal), or drawing polygons with curved lines (difficult to accelerate on GPUs), you deform the lamp-post mesh to give the appearance of a curved image. The obvious issue is that you have to massively increase the polygon count to create a smooth surface.
If this is the case, I don't think it's a new idea.
It's unclear to me how the fovea enters into it.
If this is the case, I don't think it's a new idea.
It's unclear to me how the fovea enters into it.
Can I see a video of turning/moving through a game with this? A static image isn't enough for me to tell how this would actually feel.
https://www.fovotec.com/architectural has an animation, and it looks pretty great - somehow feels more like actually 'walking' into that space.
Only forward motion, no turning in that animation. Turning is what I'm most curious about.
There is some (incredibly slow) turning in their “Automotive Visualisation”: https://www.fovotec.com/automotive
Thanks! Hard to tell from the speed, but it looks alright.
Going through the patents from the author[1] is probably the best technical resource about their secret sauce. In particular US9684946B2[2] seems to be the foundational patent about the tech.
As others mentioned already, Vedutismo/Pannini projections seem closely related here, but they might be doing some extra on top of that.
[1] https://patents.google.com/?inventor=Robert+Christian+PEPPER...
[2] https://patents.google.com/patent/US9684946B2/en
As others mentioned already, Vedutismo/Pannini projections seem closely related here, but they might be doing some extra on top of that.
[1] https://patents.google.com/?inventor=Robert+Christian+PEPPER...
[2] https://patents.google.com/patent/US9684946B2/en
This looks a lot like the quincuncial projection applied to a cube map render, with some custom logic to subtly warp geometry in a stable way so as to make certain details pop based on distance. You can try for yourself by downloading blinky (formerly fisheye quake) [1] and experimenting with the projections. That said, it's probably not just a projected cube map. Doing so will introduce noticeable sampling artifacts, so I am guessing they also do something more to address such issues.
[1] https://github.com/shaunlebron/blinky
[1] https://github.com/shaunlebron/blinky
Wow I checked that out and it's pretty close indeed. The Quake game is pretty nausea inducing but I think that's a side effect of the extreme FOVs (180+ degrees).
Of relevance, a wonderful study on the limits of human vision by NASA;
https://ntrs.nasa.gov/citations/19730006364
This image is a visual summary... all you need to know about the ‘vision frame’. The grey area is where the limits of vision and the limits of perception meet. Simply put: we can see things we can’t perceive.
https://craftofcoding.files.wordpress.com/2019/05/nasavisual...
I use this when discussing composition with design students. The problem, design is rectangle-based, human vision is not.
I became interested in this topic when I encountered a student of mine who, due to a neurological deficiency, could see things out of one eye but not understand it.
https://ntrs.nasa.gov/citations/19730006364
This image is a visual summary... all you need to know about the ‘vision frame’. The grey area is where the limits of vision and the limits of perception meet. Simply put: we can see things we can’t perceive.
https://craftofcoding.files.wordpress.com/2019/05/nasavisual...
I use this when discussing composition with design students. The problem, design is rectangle-based, human vision is not.
I became interested in this topic when I encountered a student of mine who, due to a neurological deficiency, could see things out of one eye but not understand it.
This is rather fascinating. I am shocked that such a relatively simple rendering tweak hasn't been widely used in games and movies to date.
I'm guessing that the math is not describable using a transformation matrix. It might have been too heavy for very old graphics hardware, but any GPU from the last 15 years should be able to do this kind of thing no problem.
Here's a minecraft video where you can see the effect in real-time as well as pushing the field of view too far: https://www.youtube.com/watch?v=ZNSW6Ga7Wmo
I'm guessing that the math is not describable using a transformation matrix. It might have been too heavy for very old graphics hardware, but any GPU from the last 15 years should be able to do this kind of thing no problem.
Here's a minecraft video where you can see the effect in real-time as well as pushing the field of view too far: https://www.youtube.com/watch?v=ZNSW6Ga7Wmo
I believe most of this can be emulated nicely by using lens ST maps to assign custom camera ray directions for each pixel.
Their demo seems to also offset the camera ray source a bit, so one would need another ST map for that. But all in all, this looks like someone could code a V-Ray plugin to implement it in a weekend.
I'd be truly interested to learn how this works out for them, business-wise. I don't think they can patent lens distortion and I don't think the implementation is difficult enough to protect against copycats. So how do they win the competition?
Their demo seems to also offset the camera ray source a bit, so one would need another ST map for that. But all in all, this looks like someone could code a V-Ray plugin to implement it in a weekend.
I'd be truly interested to learn how this works out for them, business-wise. I don't think they can patent lens distortion and I don't think the implementation is difficult enough to protect against copycats. So how do they win the competition?
Is my understanding correct, that this is not for 3D view of single models (like in CAD applications), but rather geared towards landscape views with high view angles >90° (in games most notably)?
Yes, they are compensating for the distortion produced by wide-angle lenses and if you don't use a wide rendering angle, e.g. in a CAD software, the effect will be minimal.
> These subtle adjustments of the 3D space are being applied volumetrically, as can be seen from the way the occlusion paths in the scene are changing. This demonstrates that the FOVO process is not simply ‘warping’ a 2D render in screen space but is applying nonlinear transformations to the entire 3D geometry.
I think this is an inexact hack to implement FOVO in rasterization engines. If raytracing were used, there would be no need to distort the geometry; you'd only need to distort the camera rays.
I think this is an inexact hack to implement FOVO in rasterization engines. If raytracing were used, there would be no need to distort the geometry; you'd only need to distort the camera rays.
It changes the occlusion paths too, so you'd also need to change the position the rays are sent from to get the same effect.
I don't think so. I agree with what teraflop wrote, emphasizing the first two paragraphs: https://news.ycombinator.com/item?id=26803097
It's pretty clear in the video that it does.
[deleted]
I currently have an AirBnb with a 180 degree panoramic view from the balcony. And I tried to take a picture with my phone. In no way did it convey how great the view is.
I have been impressed with Microsoft Labs ICE ability to stitch a good panorama together with limited distortion compensation.
https://www.microsoft.com/en-us/research/product/computation...
I mean, it isn't like the article here but it can be handy sometimes.
But honestly, nothing beats the high resolution, real time raytracing plus other senses experience of "being there". :-) Enjoy.
https://www.microsoft.com/en-us/research/product/computation...
I mean, it isn't like the article here but it can be handy sometimes.
But honestly, nothing beats the high resolution, real time raytracing plus other senses experience of "being there". :-) Enjoy.
I've been keen to add to my Hugin and AutoPano toolset.
Unfortunately the download links on that page 404.
Does anyone know where MS have moved them?
Unfortunately the download links on that page 404.
Does anyone know where MS have moved them?
Wow. My apologies, it really does seem as though MS has pulled that project doesn't it.
I did find a forum post which in turn provided a wayback machine link which got me a file. I can't install on my corporate machine to test whether the results work though and no CRC/checksum forthcoming as yet, but I will test when I get home.
https://docs.microsoft.com/en-us/answers/questions/252274/ho...
I did find a forum post which in turn provided a wayback machine link which got me a file. I can't install on my corporate machine to test whether the results work though and no CRC/checksum forthcoming as yet, but I will test when I get home.
https://docs.microsoft.com/en-us/answers/questions/252274/ho...
I hadn't considered the web archive, good call!
64bit msi installs and stitches images fine - LGTM :-)
64bit msi installs and stitches images fine - LGTM :-)
This is super interesting, would be awesome to have this implemented in, say, Blender and other common 3D software. Exciting stuff!
Huh. I’d expected to see ray tracing-based foveated rendering in this article. I’m guessing they started their company assuming rasterization was the only option, and doing slightly different warps to end up with something vaguely fovea-like.
The Wikipedia article [1] is a little light on references (there’s been fovea rendering work for decades), but at least has pointers to the modern work by NVIDIA and Oculus.
[1] https://en.m.wikipedia.org/wiki/Foveated_rendering
The Wikipedia article [1] is a little light on references (there’s been fovea rendering work for decades), but at least has pointers to the modern work by NVIDIA and Oculus.
[1] https://en.m.wikipedia.org/wiki/Foveated_rendering
This is not the same as the Foveated rendering you're thinking of.
Why would foveated rendering require raytracing? Most existing foveated rendering tech uses a traditional rasterizer and renders the periphery at a lower resolution.
So... it's just a new perspective rendering technique? I wonder how much of it is just slightly changed camera parameters.
This always bothered me when playing games, even when using much smaller FOVs. The future is bright.
You gonna need a lot bigger monitor. Actually, forget monitors, you need a projector.
For those of us with poor vision, FOVO would help a lot.
[deleted]
From [1], I’ll leave by cross posting some useful links: experimental rendering techniques with Quake [2], Minecraft [3], and a cool article about visualizing projections [4].
[0] https://www.scienceopen.com/document_file/4e8c9d47-51f4-4145...
[1] https://forums.chaosgroup.com/forum/chaos-common/chaos-commo...
[2] https://github.com/shaunlebron/blinky
[3] https://github.com/shaunlebron/flex-fov
[4] http://shaunlebron.github.io/visualizing-projections/