by Le Hui, Mingmei Cheng, Jin Xie, and Jian Yang

We use the same benchmark datasets introduced in PointNetVLAD for point cloud based place recognition, and they can be downloaded here.

• Oxford dataset
• NUS (in-house) Datasets
  • university sector (U.S.)
  • residential area (R.A.)
  • business district (B.D.)

Python 3.6+
Tensorflow 1.12
CUDA 9.0

Download the zip file of the benchmark datasets found here and extract the folder. Therefore, you have two folders: 1) benchmark_datasets/ and 2) EPC-Net/

We store the positive and negative point clouds to each anchor on pickle files that are used in our training and evaluation codes. The files only need to be generated once. The generation of these files may take a few minutes.

cd generating_queries/ 

# For training tuples in EPC-Net
python generate_training_tuples_baseline.py

# For network evaluation
python generate_test_sets.py

• To train and evaluate EPC-Net, run the following command:

  # Train
  sh sh_train.sh path_epc-net configs/epc-net.yaml
  
  # Eval
  python evaluate.py --config configs/epc-net.yaml --log_dir exp/path_epc-net --model_name model_epoch20_iter18101.ckpt
  

• To train and evaluate EPC-Net-L, run the following command:

  # Train
  sh sh_train.sh path_epc-net-l configs/epc-net-l.yaml
  
  # Eval
  python evaluate.py --config configs/epc-net-l.yaml --log_dir exp/path_epc-net-l --model_name model_epoch20_iter18101.ckpt
  

• To transfer EPC-Net model as teacher model, run the following command:

  python ckpt_transfer.py --old_ckpt exp/path_epc-net/saved_model/model_epoch20_iter18101.ckpt  --new_path exp/path_epc-net-l-d/transfer_teacher --prefix teacher
  

• To train and evaluate EPC-Net-L-D, run the following command:

  # Train
  sh sh_kd_train.sh teacher_model_epoch20_iter18101.ckpt path_epc-net-l configs/epc-net-l-d.yaml
  
  # Eval
  python kd_evaluate.py --config configs/epc-net-l-d.yaml --log_dir exp/path_epc-net-l-d --model_name student_model_epoch20_iter18101.ckpt
  

The pre-trained models for EPC-Net, EPC-Net-L, and EPC-Net-L-D have been upload in the exp/ folder:

# the pre-trained model for EPC-Net
exp/epc-net/saved_model/model_epoch22_iter18101.ckpt

# the pre-trained model for EPC-Net-L
exp/epc-net-l/saved_model/model_epoch13_iter18101.ckpt

# the pre-trained model for EPC-Net-L-D
exp/epc-net-l-d/saved_model/model_epoch20_iter18101.ckpt

# the transfer model for EPC-Net-L-D
exp/epc-net-l-d/transfer_teacher/model_epoch22_iter18101.ckpt

You can run the evaluation code to reproduce the results.

Our code refers to PointNetVLAD.