#Getting Set Up
Setting up the conda environment and downloading data and weights for the fern scene
conda env create -f environment.yml
conda activate nerf
bash download_example_data.sh
Double check that within the 'logs' directory there is a folder titled 'fern_example' Within 'fern_example' should be 'config.txt', 'args.txt', and two .npy files of model weights
#Rendering!
You can try rendering a low resolution image using render_demo.py
To render with normal nerf:
python render_demo.py --vanilla
To render using point cloud depth estimation:
python render_demo.py --cloud
To render using sparse sampling + interpolation:
python render_demo.py --interpolate
The using --cloud may lead to slow rendering. You may also pass in an integer using the --down flag. Both dimensions of the new views are divided by this number. The default is 12 and it is not recommended to go lower that 4 if you aren't using a GPU. NOTE: If you are passing --interpolate, then --down must be a product of elements of the set {2,2,3,3,7} (e.g. 2,3,4,6,9) ¯_(ツ)_/¯ it's a feature!
To visualize the depth bounds and the point cloud:
python render_demo.py --cloud_viz
First you will see near bounds, then far bounds, then the new camera center, it's view, and the full point cloud.
#Where the important things are run_nerf.py, run_nerf_helpers.py - Most network functionality for vanilla NeRF as well as the point cloud based approach run_nerf_fast.py, run_nerf_helpers_fast.py - Most network functionality the sparse sampling model point_cloud.py - Methods to generate and write point clouds, as well as to read point clouds and project them to NDC space
A variety of other scripts are used to produce our figures