conda env create -f environment.yml
conda activate grf

python run.py --data_root [path to directory with dataset] ] \
    --expname [experiment name]
    --basedir [where to store ckpts and logs]
    --datadir [input data directory]
    --netdepth [layers in network]
    --netwidth [channels per layer]
    --netdepth_fine [layers in fine network]
    --netwidth_fine [channels per layer in fine network]
    --N_rand [batch size (number of random rays per gradient step)]
    --lrate [learning rate]
    --lrate_decay [exponential learning rate decay (in 1000s)]
    --chunk [number of rays processed in parallel, decrease if running out of memory]
    --netchunk [number of pts sent through network in parallel, decrease if running out of memory]
    --no_batching [only take random rays from 1 image at a time]
    --no_reload [do not reload weights from saved ckpt]
    --ft_path [specific weights npy file to reload for coarse network]
    --random_seed [fix random seed for repeatability]
    --precrop_iters [number of steps to train on central crops]
    --precrop_frac [fraction of img taken for central crops]
    --N_samples [number of coarse samples per ray]
    --N_importance [number of additional fine samples per ray]
    --perturb [set to 0. for no jitter, 1. for jitter]
    --use_viewdirs [use full 5D input instead of 3D]
    --i_embed [set 0 for default positional encoding, -1 for none]
    --multires [log2 of max freq for positional encoding (3D location)]
    --multires_views [log2 of max freq for positional encoding (2D direction)]
    --raw_noise_std [std dev of noise added to regularize sigma_a output, 1e0 recommended]
    --render_only [do not optimize, reload weights and render out render_poses path]
    --dataset_type [options: llff / blender / shapenet / multishapenet]
    --testskip [will load 1/N images from test/val sets, useful for large datasets like deepvoxels]
    --white_bkgd [set to render synthetic data on a white bkgd (always use for dvoxels)]
    --half_res [load blender synthetic data at 400x400 instead of 800x800]
    --no_ndc [do not use normalized device coordinates (set for non-forward facing scenes)]
    --lindisp [sampling linearly in disparity rather than depth]
    --spherify [set for spherical 360 scenes]
    --llffhold [will take every 1/N images as LLFF test set, paper uses 8]
    --i_print [frequency of console printout and metric loggin]
    --i_img [frequency of tensorboard image logging]
    --i_weights [frequency of weight ckpt saving]
    --i_testset [frequency of testset saving]
    --i_video [frequency of render_poses video saving]
    --attention_direction_multires [frequency of embedding for value]
    --attention_view_multires [frequency of embedding for direction]
    --training_recon [whether to render images from the test set or not during final evaluation]
    --use_quaternion [append input pose as quaternion to input to unet]
    --no_globl [don't use global vector in middle of unet]
    --no_render_pose [append render pose to input to unet]
    --use_attsets [use attsets, otherwise use slot attention]

@inproceedings{grf2020,
  title={GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering},
  author={Trevithick, Alex and Yang, Bo},
  booktitle={arXiv:2010.04595},
  year={2020}
}