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Image Alignment/Processing Problem

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Not sure if this should be moved to the math section...

 

Suppose I have a 3D object in some reference position/orientation R.  It undergoes a, relatively small, rigid-body transform M to get in new position/orientation R'.  Then R and R' will have different projected images I and I' relative to some camera C.  I want to find the alignment transform to align I and I'.  I know there are 2D algorithms for this to estimate the transform by identifying several feature pixels on the images, and then find a 2D affine transform.

 

Question is: does knowing M help?  That is, does knowing the 3D transform help me get a more accurate image alignment algorithm or help me get it faster?

 

So far, I think it will help somewhat.  Given the known feature pixels in image I from the reference position and their 3D points on the model, I can apply M, then project back to I' so that I know the feature pixels for R'.  This saves me having to search for matching feature pixels.  But is there room for more improvement? 

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well, if R':=R*M, then M should be the alignment transformation. The projection of R and R' cancel out (unless the projection changes, but you sound like the camera C is fixed). Hence, yes, it helps, it's all you need.

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