The effect of tau about differentiable_volumetric_rendering HOT 1 CLOSED

Hi @kwea123 , thanks a lot for your interest in the project!

Yes, what you observed is indeed very interesting. The main focus of our work is to learn to reconstruct textured objects while having only posed multi-view images with object masks and possibly depth maps as supervision. We choose to represent the object implicitly, i.e. f(x) = tau. In contrast, in NeRF the main focus is multi-view consistent novel-view synthesis, also only from posed multi-view images.
I think both representations are very interesting and have pros and cons. In addition, this is a very active research area - I am also very excited to see where this line of research goes.

Regarding different threshold values, we used the obvious choice of 0.5 in this project. In our previous project Occupancy Networks, we learned implicit functions with 3D supervision and we show and ablation study in the supplementary material (e.g. Fig. 3 - 4). Here, the value 0.2 performed best. However, the loss which is optimized is different so it cannot directly be compared.

I hope this helps a little. Good luck with your research!

from differentiable_volumetric_rendering.