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https://github.com/megvii-research/PETR

[ECCV2022] PETR: Position Embedding Transformation for Multi-View 3D Object Detection & [ICCV2023] PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
https://github.com/megvii-research/PETR
3d-position-embedding multi-camera multi-task-learning object-detection segmentation
Last synced: 12 days ago
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[ECCV2022] PETR: Position Embedding Transformation for Multi-View 3D Object Detection & [ICCV2023] PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
Host: GitHub
URL: https://github.com/megvii-research/PETR
Owner: megvii-research
License: other
Created: 2022-06-06T04:01:31.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2023-10-11T05:59:40.000Z (about 1 year ago)
Last Synced: 2024-08-01T03:43:40.746Z (3 months ago)
Topics: 3d-position-embedding, multi-camera, multi-task-learning, object-detection, segmentation
Language: Python
Homepage:
Size: 1.5 MB
Stars: 836
Watchers: 14
Forks: 127
Open Issues: 74
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

Awesome-BEV-Perception - project
README

        # [ECCV2022] Position Embedding Transformation for Multi-View 3D Object Detection 

# [ICCV2023] PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images

[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2203.05625)

![visitors](https://visitor-badge.glitch.me/badge?page_id=megvii-research/PETR)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/petrv2-a-unified-framework-for-3d-perception/3d-object-detection-on-nuscenes-camera-only)](https://paperswithcode.com/sota/3d-object-detection-on-nuscenes-camera-only?p=petrv2-a-unified-framework-for-3d-perception)

This repository is an official implementation of [PETR](https://arxiv.org/abs/2203.05625) and [PETRv2](https://arxiv.org/abs/2206.01256). The flash attention version can be find from the "[flash](https://github.com/megvii-research/PETR/tree/flash)" branch.



  




PETR develops 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.  



  




PETRv2 is a unified framework for 3D perception from multi-view images. Based on PETR, PETRv2 explores the effectiveness of temporal modeling, which utilizes the temporal information of previous frames to boost 3D object detection. The 3D PE achieves the temporal alignment on object position of different frames. A feature-guided position encoder is further introduced to improve the data adaptability of 3D PE. To support for high-quality BEV segmentation, PETRv2 provides a simply yet effective solution by adding a set of segmentation queries. Each segmentation query is responsible for segmenting one specific patch of BEV map. PETRv2 achieves state-of-the-art performance on 3D object detection and BEV segmentation. 

## News

**2023.10.11** The 3D lane detection of PETRv2 has been released on [TopoMLP](https://github.com/wudongming97/TopoMLP). It support openlanev2 and won the 1st place in CVPR2023 workshop!.   

**2023.01.25** Our multi-view 3D detection framework [StreamPETR](https://github.com/exiawsh/StreamPETR) (63.6% NDS and 55.0% mAP)** without TTA and future frames.   

**2023.01.04** Our multi-modal detection framework [CMT](https://github.com/junjie18/CMT) is released on [arxiv](https://arxiv.org/pdf/2301.01283.pdf).    

**2022.11.04** The code of multi-scale improvement in PETRv2 is released.   

**2022.09.21** The code of query denoise improvement in PETRv2 is released.  

**2022.09.04** PETRv2 with VoVNet backbone and multi-scale achieves **(59.1% NDS and 50.8% mAP)**.  

**2022.08.11** PETRv2 with GLOM-like backbone and query denoise achieves **(59.2% NDS and 51.2% mAP)** without extra data.  

**2022.07.04** PETR has been accepted by ECCV 2022.  

**2022.06.28** The code of BEV Segmentation in PETRv2 is released.  

**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](https://arxiv.org/abs/2206.01256).  

**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](https://arxiv.org/abs/2203.05625).  

**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](https://github.com/WangYueFt/detr3d/blob/main/README.md), and can be constructed as the [install.md](./install.md).

* Environments  

  Linux, Python==3.6.8, CUDA == 11.2, pytorch == 1.9.0, mmdet3d == 0.17.1   

* Detection Data   

Follow the mmdet3d to process the nuScenes dataset (https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/data_preparation.md).

* Segmentation Data  

Download Map expansion from nuScenes dataset (https://www.nuscenes.org/nuscenes#download). Extract the contents (folders basemap, expansion and prediction) to your nuScenes `maps` folder.  

Then build Segmentation dataset:

  ```

  cd tools

  python build-dataset.py

  ```

  

  If you want to train the segmentation task immediately, we privided the processed data ( HDmaps-final.tar ) at [gdrive](https://drive.google.com/file/d/1uw-ciYbqEHRTR9JoGH8VXEiQGAQr7Kik/view?usp=sharing). The processed info files of segmentation can also be find at [gdrive](https://drive.google.com/drive/folders/1_C2yuh51ROF3UzId4L1itwGQVUeVUxU6?usp=sharing).

* Pretrained weights   

To verify the performance on the val set, we provide the pretrained V2-99 [weights](https://drive.google.com/file/d/1ABI5BoQCkCkP4B0pO5KBJ3Ni0tei0gZi/view?usp=sharing). The V2-99 is pretrained on DDAD15M ([weights](https://tri-ml-public.s3.amazonaws.com/github/dd3d/pretrained/depth_pretrained_v99-3jlw0p36-20210423_010520-model_final-remapped.pth)) and further trained on nuScenes **train set** with FCOS3D.  For the results on test set in the paper, we use the DD3D pretrained [weights](https://drive.google.com/drive/folders/1h5bDg7Oh9hKvkFL-dRhu5-ahrEp2lRNN). The ImageNet pretrained weights of other backbone can be found [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/model_zoo/open_mmlab.json).

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

  │     ├── HDmaps-nocover

  │     ├── ...

  ├── ckpts

  ├── README.md

  ```

## Train & inference

```bash

cd PETR

```

You can train the model following:

```bash

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:

```bash

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

```

## Visualize

You can generate the reault json following:

```bash

./tools/dist_test.sh projects/configs/petr/petr_vovnet_gridmask_p4_800x320.py work_dirs/petr_vovnet_gridmask_p4_800x320/latest.pth 8 --out work_dirs/pp-nus/results_eval.pkl --format-only --eval-options 'jsonfile_prefix=work_dirs/pp-nus/results_eval'

```

You can visualize the 3D object detection following:

```bash

python3 tools/visualize.py

```

## 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](https://pan.baidu.com/s/1-JkzOxKy4isMiiNHd20Z-w) with code `petr`.

| config            | mAP      | NDS     |training    |   config |   download |

|:--------:|:----------:|:---------:|:--------:|:--------:|:-------------:|

| PETR-r50-c5-1408x512   | 30.5%     | 35.0%    | 18hours  | [config](projects/configs/petr/petr_r50dcn_gridmask_c5.py)  |   [log](https://drive.google.com/file/d/1pXT6JltfMF0PAyG17zVcoXLJEYMKVWQr/view?usp=sharing) / [gdrive](https://drive.google.com/file/d/1c5rgTpHA98dFKmQ9BJN0zZbSuBFT8_Bt/view?usp=sharing)     |

| PETR-r50-p4-1408x512 | 31.70%     | 36.7%    | 21hours   | [config](projects/configs/petr/petr_r50dcn_gridmask_p4.py)   |   [log](https://drive.google.com/file/d/1Knoid2-ZiQhl1lcTt65SROTZiuvfTGT7/view?usp=sharing) / [gdrive](https://drive.google.com/file/d/1eYymeIbS0ecHhQcB8XAFazFxLPm3wIHY/view?usp=sharing)    

| PETR-vov-p4-800x320   | 37.8%     | 42.6%    | 17hours  | [config](projects/configs/petr/petr_vovnet_gridmask_p4_800x320.py)   |   [log](https://drive.google.com/file/d/1eG914jDVK3YXvbubR8VUjP2NnzYpDvHC/view?usp=sharing) / [gdrive](https://drive.google.com/file/d/1-afU8MhAf92dneOIbhoVxl_b72IAWOEJ/view?usp=sharing)        |

| PETR-vov-p4-1600x640 | 40.40%     | 45.5%    | 36hours   | [config](projects/configs/petr/petr_vovnet_gridmask_p4_1600x640.py)   |   [log](https://drive.google.com/file/d/1XfO5fb_Nd6jhQ3foBUG7WCz0SlTlBKu8/view?usp=sharing) / [gdrive](https://drive.google.com/file/d/1SV0_n0PhIraEXHJ1jIdMu3iMg9YZsm8c/view?usp=sharing)  

PETRv2: We provide a 3D object detection baseline and a BEV segmentation baseline with two frames. The model is trained on 8x 2080ti **without cbgs**. The processed [info files](https://drive.google.com/drive/folders/1_C2yuh51ROF3UzId4L1itwGQVUeVUxU6?usp=sharing) 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](https://pan.baidu.com/s/10IaWAq1mljX5ztLzQT_4Kg) with code `petr`.

| config            | mAP      | NDS     |training    |   config |   download |

|:--------:|:----------:|:---------:|:--------:|:--------:|:-------------:|

| PETRv2-vov-p4-800x320   | 41.0%     | 50.3%    | 30hours  | [config](projects/configs/petrv2/petrv2_vovnet_gridmask_p4_800x320.py)  | [log](https://drive.google.com/file/d/1QcVSDHoUAcFLqziwZrBn5A2oAjH86WiO/view?usp=sharing) / [gdrive](https://drive.google.com/file/d/1tv_D8Ahp9tz5n4pFp4a64k-IrUZPu5Im/view?usp=sharing)    

| config            | Drive      | Lane   |  Vehicle     |backbone   |   config |download  |

|:--------:|:----------:|:---------:|:--------:|:--------:|:--------:|:-------------:|

| PETRv2_BEVseg   | 85.6%     | 49.0%   | 46.3%     | V2-99  | [config](projects/configs/petrv2/PETRv2_BEVseg.py)  | [log](https://drive.google.com/drive/folders/1PdSsni_EePHlkCB-FJTIkhr979hwd8X6?usp=sharing) / [gdrive](https://drive.google.com/drive/folders/1PdSsni_EePHlkCB-FJTIkhr979hwd8X6?usp=sharing) 

| config            | F-score      | X-near   |  X-far     |  Z-near   |  Z-far   |backbone   |   config |download  |

|:--------:|:----------:|:---------:|:--------:|:--------:|:--------:|:-------------:|:-------------:|:-------------:|

| PETRv2_3DLane  | 61.2%     | 0.400   | 0.573     |0.265     |0.413    | V2-99  |  | |

StreamPETR: Stream-PETR achieves significant performance improvements without introducing extra computation cost, compared to the single-frame baseline.

| config            | mAP      | NDS     |FPS-Pytorch    |   config |   download |

|:--------:|:----------:|:---------:|:--------:|:--------:|:-------------:|

| StreamPETR-r50-704x256   | 45.0%     | 55.0%    | 31.7/s  | |  

## Acknowledgement

Many thanks to the authors of [mmdetection3d](https://github.com/open-mmlab/mmdetection3d) and [detr3d](https://github.com/WangYueFt/detr3d) .

## Citation

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

```bibtex   

@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}

}

```

```bibtex   

@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 [email protected], [email protected] or [email protected].