ispc-lab / MoNet

Source code of our paper: MoNet: Motion-Based Point Cloud Prediction Network

MoNet: Motion-based Point Cloud Prediction Network

Environments

• PyTorch 1.7.1
• cuda 11.1
• pytorch3d
• EMD

Please run the following commands to install point_utils

cd model/PointUtils
python setup.py install

Please check requirements.txt for more requirements.

Datasets

The data of the two datasets should be organized as follows:

KITTI odometry dataset

DATA_ROOT
├── 00
│   ├── velodyne
│   ├── calib.txt
├── 01
├── ...

Argoverse dataset

DATA_ROOT
├── train1
│   ├── 043aeba7-14e5-3cde-8a5c-639389b6d3a6
|       ├──lidar
|       ├──poses
|       ├──...
│   ├── ...
├── train2
├── train3
├── train4
├── val
├── test

Evaluation

Please run eval_kitti.sh/eval_argo.sh to evaluate the proposed MoNet on the two datasets using the provided pretrained model in ckpt. The ROOT, CKPT, GPU and RNN should be modified.

Train

If you want to train the network, please run train.sh and reminder to modify the ROOT, CKPT_DIR and RUNNAME.

Noting that we utilize wandb to record the training procedure, if you do not want to use it, please drop the --use_wandb in train.sh.

Citation

If you find this project useful for your work, please consider citing:

@ARTICLE{Lu_MoNet_2021,
    author={Lu, Fan and Chen, Guang and Li, Zhijun and Zhang, Lijun and Liu, Yinlong and Qu, Sanqing and Knoll, Alois},
    journal={IEEE Transactions on Intelligent Transportation Systems},
    title={MoNet: Motion-Based Point Cloud Prediction Network}, 
    year={2021},
    volume={},
    number={},
    pages={1-11}
}