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mesh-fitting-instant3d.yaml
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mesh-fitting-instant3d.yaml
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name: "mesh-fitting-nerf-instant3d"
tag: "${rmspace:,_}"
exp_root_dir: "outputs"
seed: 0
data_type: "random-camera-datamodule"
data:
batch_size: [4, 2, 1]
width: [64, 128, 256]
height: [64, 128, 256]
resolution_milestones: [500, 1000]
camera_distance_range: [1.5, 1.5]
elevation_range: [0, 0]
light_sample_strategy: "magic3d"
fovy_range: [49.13434264120263, 49.134342641202634]
eval_camera_distance: 1.5
eval_fovy_deg: 49.13434264120263
system_type: "mesh-fitting-system"
system:
geometry_type: "implicit-volume"
geometry:
radius: 1.0
normal_type: "analytic"
density_bias: "blob_magic3d"
density_activation: softplus
density_blob_scale: 10.
density_blob_std: 0.5
pos_encoding_config:
otype: HashGrid
n_levels: 16
n_features_per_level: 2
log2_hashmap_size: 19
base_resolution: 16
per_level_scale: 1.447269237440378 # max resolution 4096
material_type: "no-material"
material:
n_output_dims: 3
color_activation: sigmoid
requires_normal: true
background_type: "solid-color-background"
background:
n_output_dims: 3
color: [1, 1, 1]
# random_aug: true
renderer_type: "nerf-volume-renderer"
renderer:
radius: ${system.geometry.radius}
num_samples_per_ray: 512
guidance_type: "mesh-fitting-guidance"
guidance:
geometry_type: "mesh-fitting-obj-mesh"
geometry:
shape_init: ???
shape_init_params: 1.0
radius: 1.0 # consistent with coarse
shape_init_mesh_up: "+z"
shape_init_mesh_front: "+x"
material_type: "no-material"
material:
n_output_dims: 3
color_activation: sigmoid
background_type: "solid-color-background"
background:
n_output_dims: 3
color: [1, 1, 1]
# random_aug: true
renderer_type: "mesh-fitting-renderer"
renderer:
context_type: cuda
loggers:
wandb:
enable: false
project: "threestudio"
name: None
loss:
lambda_l1: 100.
lambda_mask: 100.
lambda_normal: 0.
lambda_sparsity: 0.
lambda_eikonal: 0.
lambda_geometry: 0.
lambda_orient: 0.5
optimizer:
name: Adam
args:
lr: 0.1
betas: [0.9, 0.99]
eps: 1.e-15
params:
geometry:
lr: 0.01
trainer:
max_steps: 1000
log_every_n_steps: 1
num_sanity_val_steps: 1
val_check_interval: 100
enable_progress_bar: true
precision: 16-mixed
checkpoint:
save_last: true
save_top_k: -1
every_n_train_steps: ${trainer.max_steps}