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CUE-Segmentation

Environment (Docker)

  • Ubuntu 18.04
  • PyTorch 1.10 + CUDA 11.1
  • MinkowskiEngine 0.5.4
# Modify `TORCH_CUDA_ARCH_LIST` in docker/Dockfile to match your GPU, then run:

$local: docker build -t cue:1.0 docker
$local: docker run --gpus all --rm -itd --name cue -v /local_dir:/container_dir --shm-size 16G --ipc=host cue:1.0
$container: conda init
$(base)container: cd cue_feature/
$(base)container: ./install_env.sh

# Then modify `/opt/conda/envs/fpt/lib/python3.8/site-packages/torch/utils/tensorboard/__init__.py` by:
# removing: LooseVersion = distutils.version.LooseVersion
# adding: from distutils.version import LooseVersion

Dataset

  • Download ScanNetV2 dataset and preprocess it:

    ./download_scannet.sh
python src/data/preprocess_scannet.py

Training and Evaluation

  • Mink

    python train.py --config=config/scannet/train_res16unet34c.gin
python eval.py  --config=config/scannet/eval_res16unet34c.gin --ckpt_path=xxx.ckpt

  • Mink+CUE

    python train.py --config=config/scannet/train_res16unet34c_prob.gin  --gpus=0
python eval.py --config=config/scannet/eval_res16unet34c_prob.gin --ckpt_path=xxx.ckpt

  • Mink+CUE+:

    python  train.py  --config=config/scannet/train_res16unet34c_probmg.gin  --gpus=2
python  eval.py --config=config/scannet/eval_res16unet34c_probmg.gin --ckpt_path=xxx.ckpt

  • Mink+SE:

    python eval.py  --config=config/scannet/eval_res16unet34c.gin --ckpt_path=xxx.ckpt

  • Mink+AU (Aleatoric Uncertainty)

    python  train.py  --config=config/scannet/train_res16unet34c_aleatoric.gin  --gpus=2
python  eval.py  --config=config/scannet/eval_res16unet34c_aleatoric.gin  --ckpt_path=xxx.ckpt

  • Mink+MCD (MC Dropout Uncertainty)

    python train.py  --config=config/scannet/train_res16unet34c_mc.gin  --gpus=1
python eval.py --config=config/scannet/eval_res16unet34c_mc.gin --ckpt_path=xxx.ckpt

  • Mink+DUL

    python train.py  --config=config/scannet/train_res16unet34c_dul.gin  --gpus=0
python eval.py --config=config/scannet/eval_res16unet34c_dul.gin --ckpt_path=xxx.ckpt

  • Mink+RUL

    python train.py  --config=config/scannet/train_res16unet34c_rul.gin  --gpus=2
python eval.py --config=config/scannet/eval_res16unet34c_rul.gin --ckpt_path=xxx.ckpt

Quantative and Qualitative Visualization

First, complete the eval.py script. Then, populate the variables in src/mbox/com.py. Follow the steps below for quantitative and qualitative visualization.

  • To calculate Expected Calibration Error (ECE), run python src/mbox/qn_sigma.py --uncertainty_method=[method]. The ece folder will be created in the [log_method] directory.
  • To visualize ECE, populate the variables and run python src/mbox/plot_ece_break.py. The meta folder will be created in the [log_method] directory.
  • To view point cloud visualization, run python src/mbox/qa_sigma.py --uncertainty_method=[method].

Pretained models

Pretained models available at Dropbox.