PETR: Position Embedding Transformation for Multi-View 3D Object Detection

This repository is an official implementation of PETR: Position Embedding Transformation for Multi-View 3D Object Detection.

In this paper, we develop position embedding transformation (PETR) for multi-view 3D object detection. PETR encodes the position information of 3D coordinates into image features, producing the 3D position-aware features. Object query can perceive the 3D position-aware features and perform end-to-end object detection. It can serve as a simple yet strong baseline for future research.

News

2022.06.16 The code of 3D object detection in PETRv2 is released.
2022.06.10 The code of PETR is released.
2022.06.06 PETRv2 is released on arxiv.
2022.06.01 PETRv2 achieves another SOTA performance on nuScenes dataset (58.2% NDS and 49.0% mAP) by the temporal modeling and supports BEV segmentation.
2022.03.10 PETR is released on arxiv.
2022.03.08 PETR achieves SOTA performance (50.4% NDS and 44.1% mAP) on standard nuScenes dataset.

Preparation

This implementation is built upon detr3d, and can be constructed as the install.md.

Environments
Linux, Python==3.6.8, CUDA == 11.2, pytorch == 1.9.0, mmdet3d == 0.17.1
Data
Follow the mmdet3d to process the nuScenes dataset (https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/data_preparation.md).
Pretrained weights
To verify the performance on the val set, we provide the pretrained V2-99 weights. The V2-99 is pretrained on DDAD15M (weights) and further trained on nuScenes train set with FCOS3D. For the results on test set in the paper, we use the DD3D pretrained weights. The ImageNet pretrained weights of other backbone can be found here. Please put the pretrained weights into ./ckpts/.

After preparation, you will be able to see the following directory structure:

PETR
├── mmdetection3d
├── projects
│   ├── configs
│   ├── mmdet3d_plugin
├── tools
├── data
│   ├── nuscenes
├── ckpts
├── README.md

Train & inference

cd PETR

You can train the model following:

tools/dist_train.sh projects/configs/petr/petr_r50dcn_gridmask_p4.py 8 --work-dir work_dirs/petr_r50dcn_gridmask_p4/

You can evaluate the model following:

tools/dist_test.sh projects/configs/petr/petr_r50dcn_gridmask_p4.py work_dirs/petr_r50dcn_gridmask_p4/latest.pth 8 --eval bbox

Main Results

PETR: We provide some results on nuScenes val set with pretrained models. These model are trained on 8x 2080ti without cbgs. Note that the models and logs are also available at Baidu Netdisk with code petr.

config	mAP	NDS	training	config	download
PETR-r50-c5-1408x512	30.5%	35.0%	18hours	config	log / gdrive
PETR-r50-p4-1408x512	31.70%	36.7%	21hours	config	log / gdrive
PETR-vov-p4-800x320	37.8%	42.6%	17hours	config	log / gdrive
PETR-vov-p4-1600x640	40.40%	45.5%	36hours	config	log / gdrive

PETRv2: We provide a 3D object detection baseline with two frames. The model is trained on 8x 2080ti without cbgs. The processed info files contain 30 previous frames, whose transformation matrix is aligned with the current frame. The info files, models and logs are also available at Baidu Netdisk with code petr.

config	mAP	NDS	training	config	download
PETRv2-vov-p4-800x320	41.0%	50.3%	30hours	config	log / gdrive

Acknowledgement

Many thanks to the authors of mmdetection3d and detr3d .

Citation

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

@article{liu2022petr,
  title={Petr: Position embedding transformation for multi-view 3d object detection},
  author={Liu, Yingfei and Wang, Tiancai and Zhang, Xiangyu and Sun, Jian},
  journal={arXiv preprint arXiv:2203.05625},
  year={2022}
}

@article{liu2022petrv2,
  title={PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images},
  author={Liu, Yingfei and Yan, Junjie and Jia, Fan and Li, Shuailin and Gao, Qi and Wang, Tiancai and Zhang, Xiangyu and Sun, Jian},
  journal={arXiv preprint arXiv:2206.01256},
  year={2022}
}

Contact

If you have any questions, feel free to open an issue or contact us at liuyingfei@megvii.com or wangtiancai@megvii.com.

gaopengpjlab/PETR