I've been reading a paper about using Matrix Completion for Photometric Stereo but I am having some troubles in section 2.2 trying to understand why irrespective of the number of pixels and the number of images $$rank(\mathbf{O}) = 3$$ where $\mathbf{O}$ is the matrix with the images linearized and stacked as column vectors.

Can someone give me a hint to understand this?


1 Answer 1


The images described in the paper are not independent, but are uniquely defined by a lighting direction which is 3 dimensional.

You could thus construct any conceivable image (in this model) by lighting the scene from 3 orthogonal directions (x, y and z for instance). This corresponds to the rank being 3.

Hope this helps.

  • $\begingroup$ Is there some other way you could explain it. I kinda understand what you are saying and based on what I've read intuitively makes sense but it will be haunting me until I really understand it. $\endgroup$
    – BRabbit27
    Apr 16, 2014 at 9:16
  • $\begingroup$ By "lighting the scene from 3 orthogonal directions" you don't mean 3 images with different incoming light-directions, do you? $\endgroup$
    – BRabbit27
    Apr 16, 2014 at 9:17
  • $\begingroup$ The other way, maybe not correct, I was thinking of it was that, any pixel has a uniquely surface-normal vector which is of dimension 3, therefore independently of the number of pixels or images we will have a unique normal, but still I am not 100% convinced of it. $\endgroup$
    – BRabbit27
    Apr 16, 2014 at 9:20
  • $\begingroup$ That's actually equivalent to what I'm saying. From 3 images with orthogonal lighting directions we could recover the surface normal in each pixel of the image. So yes, it is because the normal has dimension 3. $\endgroup$
    – LKlevin
    Apr 16, 2014 at 9:37
  • $\begingroup$ Could you give an example, maybe more math-based, in which something similar to the case I'm dealing with happens? Like I said, intuitively I see why the rank-3 but I would like a more convincing statement. $\endgroup$
    – BRabbit27
    Apr 16, 2014 at 10:37

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