> 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!
> 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.
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.
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!