This is official implementation for our AAAI2023 paper "TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry" created by Jiuming Liu, Guangming Wang, Chaokang Jiang, Zhe Liu, and Hesheng Wang.
Our model only depends on the following commonly used packages.
Package | Version |
---|---|
CUDA | 1.11.3 |
Python | 3.8.10 |
PyTorch | 1.12.0 |
h5py | not specified |
tqdm | not specified |
openpyxl | not specified |
numpy | not specified |
scipy | not specified |
Compile the furthest point sampling, grouping and gathering operation for PyTorch with following commands.
cd pointnet2
python setup.py install
We leverage CUDA-based operator for parallel computing, please compile them with following commands.
cd ops_pytorch
cd fused_conv_random_k
python setup.py install
cd ../
cd fused_conv_select_k
python setup.py install
cd ../
Datasets are available at KITTI Odometry benchmark website: https://drive.google.com/drive/folders/1Su0hCuGFo1AGrNb_VMNnlF7qeQwKjfhZ The data of the KITTI odometry dataset should be organized as follows:
data_root
├── 00
│ ├── velodyne
│ ├── calib.txt
├── 01
├── ...
Train the network by running :
python train.py
Please reminder to specify the GPU
, data_root
,log_dir
, train_list
(sequences for training), val_list
(sequences for validation) in the scripts.
You may specify the value of arguments. Please find the available arguments in the configs.py.
Evaluate the network by running :
python train.py
Please reminder to specify the GPU
, data_root
,log_dir
, test_list
(sequences for testing) in the scripts.
@inproceedings{liu2023translo,
title={TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry},
author={Liu, Jiuming and Wang, Guangming and Jiang, Chaokang and Liu, Zhe and Wang, Hesheng},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={2},
pages={1683--1691},
year={2023}
}
We thank the following open-source project for the help of the implementations: