How to train Ref-NeRF on real captured dataset correctly?

[78ij]

Sorry for opening an issue again. I am currently training Ref-NeRF on the released real captured dataset(more precisely, on 'gardenspheres' dataset). I discovered it is in LLFF format, so i modified the config blender-refnerf.gin, disables the normal metric calculation, and copied some parameters from llff_256.gin, the final config is as follows:

Config.dataset_loader = 'llff'
Config.batching = 'single_image'
Config.near = 0.
Config.far = 1.
Config.factor = 4
Config.forward_facing = True
Config.batch_size = 256
Config.eval_render_interval = 5
Config.render_chunk_size = 256
Config.compute_normal_metrics = False
Config.data_loss_type = 'mse'
Config.distortion_loss_mult = 0.0
Config.orientation_loss_mult = 0.1
Config.orientation_loss_target = 'normals_pred'
Config.predicted_normal_loss_mult = 3e-4
Config.orientation_coarse_loss_mult = 0.01
Config.predicted_normal_coarse_loss_mult = 3e-5
Config.interlevel_loss_mult = 0.0
Config.data_coarse_loss_mult = 0.1
Config.adam_eps = 1e-8

Model.num_levels = 2
Model.single_mlp = True
Model.num_prop_samples = 128  # This needs to be set despite single_mlp = True.
Model.num_nerf_samples = 128
Model.anneal_slope = 0.
Model.dilation_multiplier = 0.
Model.dilation_bias = 0.
Model.single_jitter = False
Model.resample_padding = 0.01

NerfMLP.net_depth = 8
NerfMLP.net_width = 256
NerfMLP.net_depth_viewdirs = 8
NerfMLP.basis_shape = 'octahedron'
NerfMLP.basis_subdivisions = 1
NerfMLP.disable_density_normals = False
NerfMLP.enable_pred_normals = True
NerfMLP.use_directional_enc = True
NerfMLP.use_reflections = True
NerfMLP.deg_view = 5
NerfMLP.enable_pred_roughness = True
NerfMLP.use_diffuse_color = True
NerfMLP.use_specular_tint = True
NerfMLP.use_n_dot_v = True
NerfMLP.bottleneck_width = 128
NerfMLP.density_bias = 0.5
NerfMLP.max_deg_point = 16

However, the outcome of the training is blurry:

Could you please correct my training config, or release the config used to train the real-captured dataset? Many thanks!