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  1. Also, I forgot to mention something. You were talking about how white spots are given little hills. I know it happens, and it happens because of the variation in the albedo of the surface. But that happens only if you work on a simple unprocessed image. If you were to work only on the lightning data, it would work pretty good. (It naturally happens that something that's deeper is darker, ambient occlusion etc, if you do some crazy math here you can actually recover the depth information pretty accurately)
  2. Yeah it's basically creating a pyramid of blurred greyscale input images, calculating normal maps from them (while treating them like casual depth maps), and adding them together while using some particular weight coefficients. They vary per image. But most of the time the lowest frequency band has the highest weight and it goes down, something like 1/f but not as sudden. While it works pretty good on most images, it doesn't work that good on images with sharp edges. Here's a good example: As you can see, the cracked concrete at the top has obvious sharp edges, while my algorithm generates smooth edges. I'll be working on it. 
  3. So I was playing around with this normal vector reconstruction from a single image, and I must say I'm getting pretty impressive results. Here's a normal map generated from the image from my previous post: Another example of a generated normal map: What do you think?
  4. Well, the images I'm working on are illuminated by ambient lightning (most likely sky), so no shadows, and the lightning is really smooth. Here is a good example of an image I'd be working on:   When trying to approximate depth, the dark-is-deep approach doesn't work very well because of the variations in the surface reflectance value. That's why I'm trying to find the surface reflectance value. Diving the above image by its albedo part would result in pure lightning information.
  5. I'm was struggling with this for a while. Simply said, I want to approximate the surface reflectance factor for each pixel from a single image. Let's have a look at this simple equation: Where: pi - Pixel intensity sr - Surface reflectance at the current pixel n, l - Self explanatory   Now, mathematically, it's not possible to recover sr just by having pi. So the only option here is to somehow statistically approximate it. How can I accomplish that? I mean, our brain does it everyday, so it's definitely possible to approximate it accurately. Does anybody have any ideas?

    Lighten/Darken a color

    Just multiply your color vector by a scalar. For example, 1.5 to lighten, 0.5 to darken, etc.

    Can I Bake a Position Map ?

    Just use the vertex position... hmmm?

    Vector based video


    Shadows from point lights

    You always use ortographic projection afaik.
  10. http://glsl.heroku.com/e#5286.0 The code isn't optimized in any way, it's slow. You need to optimize it. It looks pretty similar huh?
  11. Well don't use if's, because that method looks shitty. There is hard jump from black to white. This is how you make a soft threshold: vec3 colorThresholded = max(0.0, color - vec3(threshold)) * (1.0 / threshold);
  12. Assumming that your whole world is made out of quads... Is there any method for packing triangle world in to lightmap? Maybe I should just find every 2 triangles that has similar normal, and then assume those two triangles are one quad?
  13. Hey I've read somewhere about fft bokeh (lens) blur. I'm trying to implement it. I'm totally new to fft and stuff. I managed to successfully fft an image, I have an 2d array of complex numbers.. when I do a backward fft, I get the same image as before, so it works. How can I filter it to get a bokeh effect?
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