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_base_ = '../default.py'
basedir = './logs/nerf_unbounded'
data = dict(
dataset_type='llff',
spherify=True,
factor=4,
llffhold=8,
white_bkgd=True,
rand_bkgd=True,
unbounded_inward=True,
load2gpu_on_the_fly=True,
)
coarse_train = dict(N_iters=0)
fine_train = dict(
N_iters=800000,
N_rand=1024 * 4,
lrate_decay=80,
ray_sampler='flatten',
weight_nearclip=1.0,
weight_distortion=0.01,
pg_scale=[2000,4000,6000,8000,10000,12000,14000,16000],
tv_before=20000,
tv_dense_before=20000,
weight_tv_density=1e-6,
weight_tv_k0=1e-7,
)
alpha_init = 1e-4
stepsize = 0.5
fine_model_and_render = dict(
num_voxels=320**3,
num_voxels_base=160**3,
alpha_init=alpha_init,
stepsize=stepsize,
fast_color_thres={
'_delete_': True,
0 : alpha_init*stepsize/10,
1500: min(alpha_init, 1e-4)*stepsize/5,
2500: min(alpha_init, 1e-4)*stepsize/2,
3500: min(alpha_init, 1e-4)*stepsize/1.5,
4500: min(alpha_init, 1e-4)*stepsize,
5500: min(alpha_init, 1e-4),
6500: 1e-4,
},
world_bound_scale=1,
)
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