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neuralangelo.yaml
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neuralangelo.yaml
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_: &mlp_size # skip_geo_feat: True
geometry_cfg:
type: SDFRegressor
width: 256
depth: 1
splits: [1, 256]
bias: 0.5
appearance_cfg:
type: ColorRegressor
width: 256
depth: 4
parameterizer_cfg:
radius: 3.0 # strange contraction artifacts?
normalize: True
dataloader_cfg:
dataset_cfg: &dataset_cfg
cache_raw: True
n_rays: 1024
near: 0.5
batch_sampler_cfg:
batch_size: 16 # will sample from all images (this should be only used for static scenes)
num_workers: 16
val_dataloader_cfg:
num_workers: 4
dataset_cfg:
<<: *dataset_cfg
runner_cfg:
epochs: 100
ep_iter: &ep_iter 5000
optimizer_cfg:
lr: 1.0e-3
eps: 1.0e-15
weight_decay: 1.0e-4
scheduler_cfg:
type: MultiStepWarmupScheduler
warm_up_end: *ep_iter
milestones: [300000, 400000]
# Always define full model config
model_cfg:
# chunkify_rays: False # faster rendering and optimization with less overhead
train_chunk_size: 8192
render_chunk_size: 8192
supervisor_cfg:
dist_loss_weight: 0.0
eikonal_loss_weight: 0.1
curvature_loss_weight: 5.0e-4
sampler_cfg:
type: NeuSSampler
n_samples: [64, 32, 32]
network_cfg:
type: NeuSNetwork
xyzt_embedder_cfg:
xyz_embedder_cfg:
type: NoopEmbedder
in_dim: 3
t_embedder_cfg:
out_dim: 8
deformer_cfg:
type: EmptyRegressor
xyz_embedder_cfg:
type: TcnnHashEmbedder # no mipnerf360 contraction
in_dim: 3
# dtype: half
bounds: [[-1.2, -1.2, -1.2], [1.2, 1.2, 1.2]]
n_levels: 16
# n_features_per_level: 8
n_features_per_level: 4
# b: 1.3195079108
b: 1.3819128800
# log2_hashmap_size: 22
log2_hashmap_size: 18
# base_resolution: 64
base_resolution: 16
interpolation: 'Linear'
make_mask: True
<<: *mlp_size
geo_use_xyzt_feat: False
use_finite_diff: True
use_finite_diff_schedule: True
use_hash_encoding_schedule: True
level_init: 4
steps_per_level: *ep_iter
use_curvature_loss_weight_schedule: True