Why not apply positional encoding to positions in the rendering network?

[Kai-46]

Hi,

I have a quick question regarding the design choice made in this work. In the rendering network, positional encoding is applied to view directions but not positions. This is quite different from NeRF, where they positional-encode the 3d positions. I just wonder if there's any special consideration behind this choice? Thank you.

[lioryariv]

Hi,
Note that we do apply positional encoding (PE) to the 3D position rendering the implicit network, which represents the geometry.
We did try applying PE also on the position entering the rendering network, but it didn't change the results drastically (haven't check that carefully).
There is also a sense of a design choice here since the 3D position entering the rendering network is a function of the geometry: x(theta) (see equation 3 in the paper), so it is composed of applying PE to the 3D position. So when backpropagating, the high frequencies learned in the geometry, that's the intuition behind this choice.

[RanjithKatta]

Hello,

I tried to run the code and I am facing using what I should use for SCAN_ID , I did try the scan24(folder name of DTU ) and with 24 ( int of folder) and I did try making both DTU and code exists in same folder, but I am unable to train the network. After downloading the datasets there is no instruction in folder the DTU need to saved for training the network.

Whether it need to saved into the datasets folder of subfolder of code or to a folder called data. Please give me a clarity.

[lioryariv]

Hi,
(I'm afraid it is a new issue, but nevermind)

First of all, the DTU folder should be in the 'data' folder, and this is exactly what happens when running:
bash data/download_data.sh

Regarding the SCAN_ID it is simply a string number, for example:
python training/exp_runner.py --conf ./confs/dtu_fixed_cameras.conf --scan_id 65

Hope it will work out!
Best
Lior