How to reproduce the results of the paper? #28
Description
Thanks for your released code.
When conducting experiments on the Horse scene, Francis scene and Church scene of the Tanks dataset using the default.yaml file, I found that the results were much worse than those in the paper. PSNR, SSIM were worse than those in the paper. How can I set up the yaml file to get better results? The default.yaml was configured as follow:
model:
num_layers: 8
freeze_network: False
use_image_feature: False
network_type: official
occ_activation: softplus
hidden_dim: 256
pos_enc_levels: 10
dir_enc_levels: 4
dataloading:
dataset_name: any
path:
scene: []
batchsize: 1
n_workers: 1
img_size:
path:
with_depth: False
with_mask: False
spherify: True
customized_poses: False #use poses other than colmap
customized_focal: False #use focal other than colmap
resize_factor:
depth_net: dpt
crop_size: 0
random_ref: 1
norm_depth: False
load_colmap_poses: True
shuffle: True
sample_rate: 8
rendering:
type: nope_nerf # changed
n_max_network_queries: 64000
white_background: False
radius: 4.0
num_points: 128
depth_range: [0.01, 10]
dist_alpha: False
use_ray_dir: True
normalise_ray: True
normal_loss: False
sample_option: uniform
outside_steps: 0
depth:
type: None
path: DPT/dpt_hybrid-midas-501f0c75.pt
non_negative: True
scale: 0.000305
shift: 0.1378
invert: True
freeze: True
pose:
learn_pose: True
learn_R: True
learn_t: True
init_pose: False
init_R_only: False
learn_focal: False
update_focal: True
fx_only: False
focal_order: 2
init_pose_type: gt
init_focal_type: gt
distortion:
learn_distortion: True
fix_scaleN: True
learn_scale: True
learn_shift: True
training:
type: nope_nerf
load_dir: model.pt
load_pose_dir: model_pose.pt
load_focal_dir: model_focal.pt
load_distortion_dir: model_distortion.pt
n_training_points: 1024
scheduling_epoch: 10000
batch_size: 1
learning_rate: 0.001
focal_lr: 0.001
pose_lr: 0.0005
distortion_lr: 0.0005
weight_decay: 0.0
scheduler_gamma_pose: 0.9
scheduler_gamma: 0.9954
scheduler_gamma_distortion: 0.9
scheduler_gamma_focal: 0.9
validate_every: -1
visualize_every: 10000
eval_pose_every: 1 # epoch
eval_img_every: 1 # epoch
print_every: 100
backup_every: 10000
checkpoint_every: 5000
rgb_weight: [1.0, 1.0]
depth_weight: [0.04, 0.0]
weight_dist_2nd_loss: [0.0, 0.0]
weight_dist_1st_loss: [0.0, 0.0]
pc_weight: [1.0, 0.0]
rgb_s_weight: [1.0, 0.0]
depth_consistency_weight: [0.0, 0.0]
rgb_loss_type: l1
depth_loss_type: l1
log_scale_shift_per_view: False
with_auto_mask: False
vis_geo: True
vis_resolution: [54, 96]
mode: train
with_ssim: False
use_gt_depth: False
load_ckpt_model_only: False
optim: Adam
detach_gt_depth: False
match_method: dense
pc_ratio: 4
shift_first: False
detach_ref_img: True
scheduling_start: 10000
auto_scheduler: True
length_smooth: 1000
patient: 30
scale_pcs: True
detach_rgbs_scale: False
scheduling_mode:
vis_reprojection_every: 5000
nearest_limit: 0.01
annealing_epochs: 2000 #should be >=0
extract_images:
extraction_dir: extraction
N_novel_imgs: 120
traj_option: bspline
use_learnt_poses: True
use_learnt_focal: True
resolution:
model_file: model.pt
model_file_pose: model_pose.pt
model_file_focal: model_focal.pt
eval_depth: False
bspline_degree: 100
eval_pose:
n_points: 1024
type: nope_nerf
type_to_eval: eval
opt_pose_epoch: 1000
extraction_dir: extraction
init_method: pre
opt_eval_lr: 0.001