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train{
folderprefix = ""
input_path = ../data/input_data/
fileprefix_list = [
broken_bullet_50k, # more input models can be added here
]
d_in = 3 # 输入数据的维度。在3D点云数据中,通常为3(x、y、z坐标)
plot_frequency = 5000 # 每5000次迭代绘制一次点云
checkpoint_frequency = 5000
status_frequency = 100
weight_decay = 0
learning_rate_schedule = [{
"Type" : "Step", # 学习率调度类型。"Step"表示在指定迭代次数后将学习率乘以因子
"Initial" : 0.005,
"Interval" : 2000,
"Factor" : 0.5
}]
network_class = model.network.NHRepNet # 网络类型。NHRepNet是neural halfspace representation network的缩写
}
plot{
resolution = 128 # 体素网格的分辨率。128表示体素网格的每个维度有128个单元格
mc_value = 0.0 # 体素网格的体素值。0.0表示体素网格的体素值为0
is_uniform_grid = True
verbose = False
save_html = False
save_ply = True
overwrite = True
}
network{
inputs{
dims_sdf = [256, 256, 256] # 体素网格的维度。[256, 256, 256]表示体素网格的每个维度有256个单元格
skip_in = [] # 跳过输入的索引。[]表示不跳过任何输入
geometric_init= True # 几何初始化。True表示使用几何初始化
radius_init = 1 # 半径初始化。1表示半径初始化为1
beta=100 # beta值。100表示beta值为100
}
sampler{
sampler_type = NormalPerPoint # 采样器类型。NormalPerPoint表示每个点都使用正态分布采样
properties{
global_sigma = 1.8 # 全局sigma值。1.8表示全局sigma值为1.8
}
}
loss{
lambda = 1 # 损失函数中的lambda值。1表示lambda值为1
normals_lambda = 1 # 损失函数中的normals_lambda值。1表示normals_lambda值为1
}
}