[Question] About the derivation of normal vector from the density field

[costrice]

In the released paper Neural Microfacet Fields for Inverse Rendering (ICCV'23), it was mentioned in Section C of the appendix that

To calculate the normal vectors of the density field, we apply a finite difference kernel, convolved with a 3×3 Gaussian smoothing kernel with σ = 1, then linearly interpolate between samples to get the resulting gradient in the 3D volume.

I appreciate this idea to use smoothed gradient as the shading normal, and want to check the code that implements it. However, I failed to find the code that implements this finite difference and gaussian filtering functionality.

The forward method of the TensorNeRF class in modules/tensor_nerf.py seems to be using the compute_normals method of the TensorBase class, but this method directly use analytically derived gradients, rather than gradients from finite difference, without doing Gaussian filtering.

Then, I searched the project for 'normal' and found another function called compute_density_norm in fields/triplanar.py that seems doing finite difference (I'm not sure). But this class seems to be not in active use.

I would be very grateful if the authors could refer me to the code implementing the normal derivation described in the paper. Thanks in advance!

[half-potato]

The gradient calculation can be found here: modules/grid_sample_Cinf.py

This special grid sample operator is used here: fields/tensoRF.py

[costrice]

Thanks very much for your immediate and helpful reply!