- Better geometry-to-image registration: the current geometry to image registration is no better than the user input which is a problem. We should use the user input as a starting point to some more general optimization.
- More complex surfaces: In particular we should try out surface light fields on objects with vague surfaces like stuffed animals because these are objects that have been successfully modeled using light field / lumigraph / view-dependent texture mapping techniques.
- Derive geometry from images: As I mentioned, scanning specular objects is not particular easy. It would be good to skip this step by deriving the geometry directly from the images using some sort of multiview stereo algorithm. Unfortunately stereo isn't particularly easy to apply to shiny objects either, so this could be a challenging and interesting area of future work.
- Combining FQ and PFA: Function quantization uses a set of 0-dimensional subspaces to approximate the data, and PFA uses a single higher-dimensional space. If we use a set of higher-dimensional spaces (aka non-linear regression) we can hopefully achieve the best of both worlds.