configs/train_semantic_kitti_360.yaml

defaults:
  - dataset: kitti_360_sscbench
  - model: dino_downsampler
  - renderer: pixelnerf
  - training: semantic
  - validation: semantic
  - downstream: semantic
  - _self_

training_type: "downstream_training"
mode: "nvs"
seed: 0
backend: null
nproc_per_node: null
with_amp: true
name: "training"
batch_size: 4
gradient_accum_factor: 1
num_workers: 6

renderer:
  n_coarse : 32
  n_fine : 0
  n_fine_depth : 0
  depth_std : 1.0
  sched : []
  white_bkgd : false
  lindisp: true
  hard_alpha_cap: true
  render_mode: volumetric
  eval_batch_size: 65536
  normalize_dino: true

output:
  path: "out/ssc-paper"
  unique_id: ssc-kitti-360-sscbench

training:
  epoch_length: 1000
  resume_from: "<PATH-FEATURE-CHECKPOINT>.pt"

  optimizer:
    args:
      lr: 5e-4

model:
  sample_radius_3d: 0.5

downstream:
  input_dim: 768
  mode: "3d"
  # mlp_head: true