result_name: partfield_features/correspondence_demo

continue_ckpt: model/model.ckpt

triplane_channels_low: 128
triplane_channels_high: 512
triplane_resolution: 128

vertex_feature: True
n_point_per_face: 1000
n_sample_each: 10000
is_pc: True
remesh_demo: False
correspondence_demo: True

preprocess_mesh: True

dataset:
  type: "Mix"
  data_path: data/DenseCorr3D
  train_batch_size: 1
  val_batch_size: 1
  train_num_workers: 8
  all_files:
    # pairs of example to run correspondence
    - animals/071b8_toy_animals_017/simple_mesh.obj
    - animals/bdfd0_toy_animals_016/simple_mesh.obj
    - animals/2d6b3_toy_animals_009/simple_mesh.obj
    - animals/96615_toy_animals_018/simple_mesh.obj
    - chairs/063d1_chair_006/simple_mesh.obj
    - chairs/bea57_chair_012/simple_mesh.obj
    - chairs/fe0fe_chair_004/simple_mesh.obj
    - chairs/288dc_chair_011/simple_mesh.obj
    # consider decimating animals/../color_mesh.obj yourself for better mesh topology than the provided simple_mesh.obj
    # (e.g. <50k vertices for functional map efficiency).

loss:
  triplet: 1.0

use_2d_feat: False
pvcnn:
  point_encoder_type: 'pvcnn'
  z_triplane_channels: 256
  z_triplane_resolution: 128