0050.yaml

task: manhattan_sdf
exp_name: 'scannet_0050'
gpus: [0]

train_dataset_module: lib.datasets.scannet
test_dataset_module: lib.datasets.scannet
network_module: lib.networks.network
trainer_module: lib.train.trainers.manhattan_sdf
evaluator_module: lib.evaluators.mesh

model:
    net_width: 256
    feature_width: 256
    bounding_radius: 2.0
    sdf:
        geometric_init: True
        radius_init: 1.0
        net_depth: 8
        skips: [4]
        fr_pos: 6
        weight_norm: True
    radiance:
        net_depth: 4
        skips: []
        fr_pos: -1
        fr_view: -1
        weight_norm: True
    semantic:
        net_depth: 4
        skips: []
        fr_pos: -1
        fr_view: -1
        weight_norm: True
    beta_init: 0.1
    speed_factor: 10.0

train_dataset:
    data_root: 'data/scannet'
    split: 'train'
    scene: '0050_00'
    near: 0.0
    far: 2.0

test_dataset:
    data_root: 'data/scannet'
    split: 'test'
    scene: '0050_00'
    near: 0.0
    far: 2.0
    scale: 0.40543
    offset: [4.24910, 2.30138, 1.15986]

sample:
    N_samples: 128
    N_importance: 64
    rayschunk: 65536
    netchunk: 1048576
    max_upsample_steps: 6
    max_bisection_steps: 10
    epsilon: 0.1
  
train:
    N_rays: 1024
    batch_size: 1
    lr: 0.0005
    weight_decay: 0.
    epoch: 50
    scheduler:
        type: 'exponential'
        gamma: 0.1
        decay_epochs: 50
    num_workers: 0

loss:
    rgb_weight: 1.0
    depth_weight: 1.0
    depth_weight_decay: 0.5
    depth_weight_decay_epochs: [5, ]
    depth_loss_clamp: 0.5
    depth_loss_clamp_epoch: 1
    joint_start: 1
    joint_weight: 0.05
    ce_weight: 1.0
    ce_weight_decay: 0.5
    ce_weight_decay_epochs: [1, 5]
    non_plane_weight: 0.2
    eikonal_weight: 0.1

ep_iter: 1000
save_ep: 1
eval_ep: 1
save_latest_ep: 1
log_interval: 1