| # General LLFF settings | |
| Config.dataset_loader = 'llff' | |
| Config.near = 0. | |
| Config.far = 1. | |
| Config.factor = 4 | |
| Config.forward_facing = True | |
| PropMLP.disable_density_normals = True # Turn this off if using orientation loss. | |
| PropMLP.disable_rgb = True | |
| NerfMLP.disable_density_normals = True # Turn this off if using orientation loss. | |
| NerfMLP.max_deg_point = 16 | |
| PropMLP.max_deg_point = 16 | |
| Config.train_render_every = 5000 | |
| ########################## RawNeRF specific settings ########################## | |
| Config.rawnerf_mode = True | |
| Config.data_loss_type = 'rawnerf' | |
| Config.apply_bayer_mask = True | |
| Model.learned_exposure_scaling = True | |
| Model.num_levels = 2 | |
| Model.num_prop_samples = 128 # Using extra samples for now because of noise instability. | |
| Model.num_nerf_samples = 128 | |
| Model.opaque_background = True | |
| Model.distinct_prop = False | |
| # RGB activation we use for linear color outputs is exp(x - 5). | |
| NerfMLP.rgb_padding = 0. | |
| NerfMLP.rgb_activation = @math.safe_exp | |
| NerfMLP.rgb_bias = -5. | |
| PropMLP.rgb_padding = 0. | |
| PropMLP.rgb_activation = @math.safe_exp | |
| PropMLP.rgb_bias = -5. | |
| ## Experimenting with the various regularizers and losses: | |
| Config.interlevel_loss_mult = .0 # Turning off interlevel for now (default = 1.). | |
| Config.distortion_loss_mult = .01 # Distortion loss helps with floaters (default = .01). | |
| Config.orientation_loss_mult = 0. # Orientation loss also not great (try .01). | |
| Config.data_coarse_loss_mult = 0.1 # Setting this to match old MipNeRF. | |
| ## Density noise used in original NeRF: | |
| NerfMLP.density_noise = 1. | |
| PropMLP.density_noise = 1. | |
| ## Use a single MLP for all rounds of sampling: | |
| Model.single_mlp = True | |
| ## Some algorithmic settings to match the paper: | |
| Model.anneal_slope = 0. | |
| Model.dilation_multiplier = 0. | |
| Model.dilation_bias = 0. | |
| Model.single_jitter = False | |
| NerfMLP.weight_init = 'glorot_uniform' | |
| PropMLP.weight_init = 'glorot_uniform' | |
| ## Training hyperparameters used in the paper: | |
| Config.batch_size = 16384 | |
| Config.render_chunk_size = 16384 | |
| Config.lr_init = 1e-3 | |
| Config.lr_final = 1e-5 | |
| Config.max_steps = 500000 | |
| Config.checkpoint_every = 25000 | |
| Config.lr_delay_steps = 2500 | |
| Config.lr_delay_mult = 0.01 | |
| Config.grad_max_norm = 0.1 | |
| Config.grad_max_val = 0.1 | |
| Config.adam_eps = 1e-8 | |