hbb1 / pref Goto Github PK

Hello, thanks for sharing the nice and interesting work!

Since your feature encoder (PREF) is based on Fourier Transform, I guess that we can continuously express the multiple scales of the signal by changing the frequency bandwidth of the encoding volume or input signal. My questions are:

1. What is the clear difference between modifying linear_freqs and reduced_freqs of PREF and applying the gaussian kernel to input coordinates (mentioned in Figure 6. of the manuscript and also explained in Sec E of supplementary material)?
2. Can we apply both techniques in the training stage for progressive learning (coarse to fine)? Is this beneficial for PREF to express multi-scale signals without aliasing effect as BACON did (similar to Fig.4 of BACON's manuscript)?
3. What is the role of alpha of PREF?

Sorry for asking a lot of questions. I am looking for the possibility of whether this work is the voxel-based nerf that can continuously encode signals of multi-scales (For example, by varying sigma in Gaussian Filter). I am also curious about your personal opinion regarding this.

Thanks a lot!

Hi! Thank you for such interesting work and for sharing it! May I ask you a question about the paper: is it any special meaning behind d? or it is just a typo and d=n always. Because I expected P to be of dimension k x N^n = k x N x N x ... x N