What is the difference between Gauss Bilateral and Learned Bilateral?
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Hello. Thank you for sharing this awesom works. I have a question about the result in the paper.
If we do not consider the implementation, is the difference between gauss bilateral `` and learned bilateral is that the variance of filter in learned bilateral ``` is learnable and is learned from training ? Thanks!
Sorry for my delayed response. Somehow, I missed this.
'Gauss bilateral' refers to Gaussian filter in high-dimensional space. 'Learned bilateral' filter refers to learned filter weights (not just the variance of the filter). You can think of this as learning a high-dimensional convolution filter, just like in standard CNNs. Standard CNNs operate in 2D or 3D space and this work makes it feasible to do convolution in higher dimensional spaces.
@varunjampani Thanks for your reply. Maybe I have misunderstanding... Do you mean that the learned bilateral filter is not just variance learnable but the weight ? Therefore it directly output the weight, and has no need to calculate the exponential of feature difference and then normalize?
Yes, the 'learned' bilateral filter is not just learned variance, but the filter weights in high-dimensional space. You can think of this as a general convolution in high-dimensions. For both non-learned Gaussian and learned filters, we use normalization to account for non-uniformly sampled sparse data in high-dimensions.
Thanks for your reply.