Please follow the command at Data Preprocessing Documentations to process Waymo Open Dataset and install EFG
.
The preprocessed detection boxes of CenterPoint and MPPNet can be download form Google Drive and put the download files in the EFG/datasets/waymo/
folder.
Finally, compile the evaluation metrics tool provided by Waymo officials by following Quick Guide to Waymo Open Dataset.
cd playground/tracking.3d/waymo/trajectoryformer.motionpred;
efg_run --nug-gpus 8 task train
The trained model will be saved at ./log/model_final.pth
.
# CenterPoint
cd playground/tracking.3d/waymo/trajectoryformer.centerpoint;
efg_run --num-gpus 8 task train
# MPPNet
cd playground/tracking.3d/waymo/trajectoryformer.mppnet;
efg_run --num-gpus 8 task train
Set the metrics tool path, such as /home/user/bazel_bin/waymo_open_dataset/metrics/tools/compute_tracking_main
to the yaml.
For CenterPoint,
cd playground/tracking.3d/waymo/trajectoryformer.centerpoint;
# for vehicle
efg_run --num-gpus 8 task val \
trainer.eval_metrics_path /path/to/your/tools
model.nms_thresh 0.1
model.eval_class VEHICLE
# for pedestrian
efg_run --num-gpus 8 task val \
trainer.eval_metrics_path /path/to/your/tools
model.nms_thresh 0.7 \
model.eval_class PEDESTRIAN
# for cyclist
efg_run --num-gpus 8 task val \
trainer.eval_metrics_path /path/to/your/tools
model.nms_thresh 0.7 \
model.eval_class CYCLIST
For MPPNet,
# eval vehicle, pedestrian, cyclist
cd playground/tracking.3d/waymo/trajectoryformer.mppnet;
efg_run --num-gpus 8 task val \
trainer.eval_metrics_path /path/to/your/tools
model.eval_class VEHICLE or PEDESTRIAN or CYCLIST
If you want the pretrained model, please contact chenxuesong@link.cuhk.edu.hk
.
@article{chen2023trajectoryformer,
title={TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses},
author={Chen, Xuesong and Shi, Shaoshuai and Zhang, Chao and Zhu, Benjin and Wang, Qiang and Cheung, Ka Chun and See, Simon and Li, Hongsheng},
journal={arXiv preprint arXiv:2306.05888},
year={2023}
}
@inproceedings{chen2022mppnet,
title={Mppnet: Multi-frame feature intertwining with proxy points for 3d temporal object detection},
author={Chen, Xuesong and Shi, Shaoshuai and Zhu, Benjin and Cheung, Ka Chun and Xu, Hang and Li, Hongsheng},
booktitle={European Conference on Computer Vision},
pages={680--697},
year={2022},
organization={Springer}
}