I am currently working on reconstruction of BRDF at dense grid with the given sparse samples. I've implemented the Pull-Push method stated in section 6.4 of "A Data-Driven Reflectance Model". I employed i, j, k to denote theta_Difference, Phi_Difference and theta_Half respectively.
The given samples are quite sparse and unevenly scattered. With such samples, I just got unfavorable results. Detailedly, my results are excessively smooth, and mostly higher than the closest given samples.
So here come my questions:
Whether the Pull-Push method is no good for reconstructing sparse, unevenly scattered data?
Is there any alteration to be taken for reconstructing such data? My implementation is strictly in accordance with what you have stated in "A Data-Driven Reflectance Model", and this is convincing as my colleagues have reviewed my code.
Is it equivalent to adopt interpolation methods, such as bicubic interpolation, for an approximate reconstruction?