Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix

Official implementation for our AAAI2024 paper: "Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix" [Arxiv]

🔨 Dependencies and Installation

• Pytorch >= 1.7.1

# git clone this repository
git clone https://github.com/kwwcv/SSMP
cd SSMP

Dataset

• Download the nuScenes data.

# modified the following paths in gen_data.py, gen_GSdata.py, and data_utils.py
# sys.path.append('root_path/SSMP')
# sys.path.append('root_path/SSMP/nuscenes-devkit/python-sdk/')

• Run command python data/gen_data.py to generate preprocessed BEV data for training, validating, and testing. Refer to MotionNet and python data/gen_data.py -h for detailed instructions.

• Install the ground segmentation algorithm following Patchwork++. One can also try removing the ground points by simply setting a threshold along the Z-axis.

# modified the following path in gen_GSdata.py
# patchwork_module_path = "root_path/patchwork-plusplus/build/python_wrapper"

• Run command python data/gen_GSdata.py to generate preprocessed ground-removed BEV data for training.

🔥 Training

Randomly divide the training data into labeled and unlabeled data sets.

# stage 1: train model with only labeled data
python train_stage1.py --data [bev training folder] --seed [random seed] --log

# stage two: train model with both labeled and unlabeled data
python train_stage2.py --data [bev training folder] --GSdata_root [ground removal bev training folder] \
      --resume [stage 1 trained model] --seed [random seed] --if_lr --if_bevmix --log

# Keep the same [random seed] to make sure stage 1 and stage 2 are using the same labeled data set
# when using [random seed] to randomly divide unlabeled and labeled data.

One can also use the same divided labeled and unlabeled data sets as used in the paper to train the model.

# stage 1
python train_stage1.py --data [bev training folder] --preset_semi [split file] --log

# stage 2
python train_stage2.py --data [bev training folder] --GSdata_root [ground removal bev training folder] \
      --resume [stage 1 trained model] --preset_semi [split file] --if_lr --if_bevmix --log

🎯 Evaluation

Trained model

Ratio	Path
1% (semi)	SSMP1%
5% (semi)	SSMP5%
10% (semi)	SSMP10%

Testing

python test.py --data [bev testing folder] --model [model path] \
      --log_path [path to save results] --jitter

Citation

@inproceedings{wang2024semi,
  title={Semi-supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix},
  author={Wang, Kewei and Wu, Yizheng and Pan, Zhiyu and Li, Xingyi and Xian, Ke and Wang, Zhe and Cao, Zhiguo and Lin, Guosheng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={6},
  pages={5490--5498},
  year={2024}
}

🍭 Acknowledgement

Our project is based on MotionNet

The optimal transport solver is adopted from Self-Point-Flow

License

This project is licensed under NTU S-Lab License 1.0