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CUE-Feature

Environment (Docker)

  • Ubuntu 18.04
  • PyTorch 1.12 + CUDA 11.3
  • MinkowskiEngine 0.5.4

To set up the environment, modify TORCH_CUDA_ARCH_LIST in docker/Dockfile to match your GPU, and then run the following commands:

$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

Dataset

To download the required datasets, run the following scripts:

./scripts/download_3dmatch.sh dbs/
./scripts/download_3dmatch_testbench.sh dbs/

Train CUE/CUE+

  • First, to train FCGF , run the following command: 
    python main.py  train=3dmatch_pair
  • Then, to train CUE, populate weights in conf/train/3dmatch_pair_btl with the saved checkpoint path, for example: weights: logs/HCL_0419_161400/best_val_checkpoint.pth, and run the following command: 
    python main.py train=3dmatch_pair_btl
  • Alternatively, to train CUE+, populate weights in conf/train/3dmatch_pair_mbtl with the saved checkpoint path and run the following command: 
    python main.py train=3dmatch_pair_mbtl

Evaluate CUE/CUE+

  • Eval FCGF: 
    python eval/eval_3dmatch.py  --model=[saved_checkpoint.pth] --extract_features=1    --evaluate_FMR=1
  • Eval CUE: 
    python eval/eval_3dmatch.py  --model=[saved_checkpoint.pth] --evaluate_ECE=1
  • Eval CUE+: 
    python eval/eval_3dmatch.py  --model=[saved_checkpoint.pth] --evaluate_ECE=1

Plot ECE of CUE/CUE+

  • To plot the ECE of CUE/CUE+, run the following commands: 
    python eval/ece_rg.py
# populate the ece_results.pickle path in eval/plot.ece.py and then run
python eval/plot_ece.py

Pretained models

Pretained models available at Dropbox.