The code for our paper:
RangePlace: A Hierarchical Range Image Transformer for LiDAR-based Place Recognition
Developed by Ji Li.
Thank you very much [Zhiwei Li] for reconstructing this project!
Beijing Institute of Technology
conda env create -f environment.yaml -n rangeplace
Before the network training or evaluation, run the below code to generate pickles with positive and negative point clouds for each anchor point cloud.
# Generate training tuples for the KITTI Dataset
cd datasets/pointnetvlad/
python generate_training_tuples_kitti.py --dataset_root <dataset_root_path>
# Generate evaluation tuples
python generate_test_kitti.py --dataset_root <dataset_root_path>
python generate_test_ford.py --dataset_root <dataset_root_path>
<dataset_root_path> is a path to dataset root folder, e.g. /data/kitti_datasets/.
Before running the code, ensure you have read/write rights to <dataset_root_path>, as training and evaluation pickles
are saved there.
data_root_folder (KITTI for example) follows:
├── 00
│ ├── depth_map
│ ├── 000000.png
│ ├── 000001.png
│ ├── 000002.png
│ ├── ...
│ └── pointcloud_locations.csv
├── 01
├── 02
├── ...
└── 10
To train the network, run:
cd training
python train.py --config ../config/config_kitti.txt --model_config ../models/rangplace.txt --resume <path_to_your_pretrained_weights>
Take KITTI dataset for example, run:
cd eval
python pnv_evaluate.py --config ../config/config_kitti.txt --model_config ../models/rangeplace.txt --weights <path_to_your_weights>
Our code is released under the MIT License (see LICENSE file for details).
We have intensively borrowed code from the following repositories. Many thanks to the authors for sharing their code.