If you have one image and you can determine the light direction (via shadows) then you can determine normals via n.l*p = I (p is albedo and I is intensity of the image)

I do like how that sounds, since one of the algorithms that I developed finds shadows within the image, and parents it to a light source. Then, from that I can find an estimated light direction.

Do you mind elaborating on the technique you are explaining? Perhaps provide some links

I appologize, this is the worst case you can find yourself in. In general, the solution is underdetermined because a gradient has two components for a surface and you have one equation. If you can find two highlights in your image (from different light sources) then its very easy to solve. General photometric stereo techniques require at minimum two equations. Intuitively, this means the normals can take any isotropic rotation and give the same lighting intensity. For example, imagine a ball lit given an intensity of .747 any normal that has a rotation of 45 degrees from the +Z axis would satisfy this equation.

However, that doesn't stop an algorithm from working. Given enough ingenuity and some user input you can still solve it. There has been published algorithms that do accomplish what you are looking for but its a guided process and generates depth. From depth, it's easy to get back to normals. If you are still looking to go this way then let me know and I'll dig up the paper that does this when I get home from work.

Do you have any other information? If you are working with computer vision then typically you have either 3d information or at least depth?

-= Dave

I have no form of depth information, or 3D scene information/

My algorithm detects multi-level gradient by calculating an estimated rate of decay of each visible shadow within the room. Said being, utilizing that, I can detect where shadows overlay, or where more than one show is visible.

The final objective is a bit on the sci-fi end, but seems more and more practical every day that I work on this. I want to make a 3D scanner that can work on any existing mobile device, without any form of optical modifications, or user input. Out off all the issues that I have, the 2 largest ones are Normal Approximation without any form of depth,or 3D data, and threshold approximation, so the AI can classify whether the image contains a pattern to its interest

PS: For the time being, lets pretend performance doesn't matter

The reason why I am trying to approximate normals, is because ambient occlusion requires it. My idea is that, since AO gives depth perception to video games, and special effects, why cant it give computer vision applications depth perception? I think it may come down to a matter of just solving for X

**Edited by LouisCastricato, 14 February 2013 - 03:41 PM.**