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https://github.com/AIR-THU/DAIR-RCooper

[CVPR2024] Official implementation of "RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception"
https://github.com/AIR-THU/DAIR-RCooper

autonoumous-driving cooperative-perception dataset-and-benchmark multi-view roadside-perception

Last synced: 2 months ago
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[CVPR2024] Official implementation of "RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception"

Host: GitHub
URL: https://github.com/AIR-THU/DAIR-RCooper
Owner: AIR-THU
Created: 2024-02-29T03:06:47.000Z (10 months ago)
Default Branch: main
Last Pushed: 2024-06-18T02:32:14.000Z (7 months ago)
Last Synced: 2024-07-02T18:04:32.140Z (6 months ago)
Topics: autonoumous-driving, cooperative-perception, dataset-and-benchmark, multi-view, roadside-perception
Language: Python
Homepage: https://openaccess.thecvf.com/content/CVPR2024/html/Hao_RCooper_A_Real-world_Large-scale_Dataset_for_Roadside_Cooperative_Perception_CVPR_2024_paper.html
Size: 58.9 MB
Stars: 54
Watchers: 4
Forks: 5
Open Issues: 4
Metadata Files:
- Readme: README.md

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README

        # RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception

[![paper](https://img.shields.io/badge/CVPR-Paper-green)](https://openaccess.thecvf.com/content/CVPR2024/papers/Hao_RCooper_A_Real-world_Large-scale_Dataset_for_Roadside_Cooperative_Perception_CVPR_2024_paper.pdf)

[![supp](https://img.shields.io/badge/Supp-Material-red)](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hao_RCooper_A_Real-world_CVPR_2024_supplemental.pdf)

[![arXiv](https://img.shields.io/badge/arXiv-Paper-purple)](http://arxiv.org/abs/2403.10145)

[![ckpts](https://img.shields.io/badge/ckpts-DOWNLOAD-blue)](https://drive.google.com/drive/folders/1J2nhh41UYp5jugdMT7zpxKr0CRoqpRUJ?usp=drive_link)

[![video](https://img.shields.io/badge/Youtube-Video-yellow)](https://www.youtube.com/watch?v=6CFi9Bz4wg4)

[![poster](https://img.shields.io/badge/Poster-Presentation-cyan)](assets/RCooper.jpg)

This is the official implementation of CVPR2024 paper. "RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception".

[Ruiyang Hao^*](https://ry-hao.top/),  [Siqi Fan^*](https://leofansq.github.io/), [Yingru Dai](https://dblp.org/pid/350/9258.html), [Zhenlin Zhang](https://www.linkedin.com/in/zhenlinzhangtim/), [Chenxi Li](),  [Yuntian Wang](), [Haibao Yu](https://scholar.google.com/citations?user=JW4F5HoAAAAJ), [Wenxian Yang](https://scholar.google.com/citations?user=Kiz73xwAAAAJ), [Jirui Yuan](https://air.tsinghua.edu.cn/en/info/1012/1219.htm), [Zaiqing Nie^†](https://scholar.google.com/citations?user=Qg7T6vUAAAAJ)







## Overview

- [Data Download](#data-download)

- [Data Conversion](#data-conversion)

- [Quick Start](#quick-start)

- [Benchmark](#benchmark)

- [Citation](#citation)

- [Acknowledgment](#acknowledgment)

## Data Download

Please check the bottom of this page [website](https://www.t3caic.com/qingzhen/) to download the data. As shown in the figure bellow.







After downloading the data, please put the data in the following structure:

```shell

├── RCooper

│   ├── calib

|      |── lidar2cam

|      |── lidar2world

│   ├── data

|      |── folders named specific scene index

│   ├── labels

|      |── folders named specific scene index

│   ├── original_label

|      |── folders named specific scene index

```

## Data Conversion

To facilitate the research of cooperative perception methods on RCooper. We provide the format converter from RCooper to other popular public cooperative perception datasets. After the conversion, researchers can directly employ the methods using several opensourced frameworks.

We now support the following conversions:

* [DAIR-V2X](https://github.com/AIR-THU/DAIR-V2X)

* [V2V4Real](https://github.com/ucla-mobility/V2V4Real)

* [OPV2V](https://github.com/ucla-mobility/OpenCOOD)

### RCooper to V2V4Real

Setup the dataset path in codes/dataset_convertor/converter_config.py, and complete the conversion.

```bash

cd codes/dataset_converter

python rcooper2vvreal.py

```

### RCooper to OPV2V

Setup the dataset path in codes/dataset_convertor/converter_config.py, and complete the conversion.

```bash

cd codes/dataset_converter

python rcooper2opv2v.py

```

### RCooper to DAIR-V2X

Setup the dataset path in codes/dataset_convertor/converter_config.py, and complete the conversion.

```bash

cd codes/dataset_converter

python rcooper2dair.py

```

## Quick Start

For detection training & inference, you can find instructions in [docs/corridor_scene](docs/corridor_scene) or [docs/intersection_scene](docs/intersection_scene) in detail. (Notes: you may need to set PYTHONPATH to call modified codes other than the pip-installed ones.)

For Tracking, you can find instructions in [docs/tracking.md](docs/tracking.md) in detail.

All the checkpoints are released in link in the tabels below, you can save them in [codes/ckpts/](codes/ckpts/).

## Benchmark

### Results of Cooperative 3D object detection for corridor scenes

| Method                                         | [email protected]   | [email protected]   | [email protected]   | Download Link                                                                                  |

| ---------------------------------------------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- |

| No Fusion                                      | 40.0     | 29.2     | 11.1     | [url](https://drive.google.com/drive/folders/1mmnIf0HDjS_vL1abptXM91pJHE3BLdqT?usp=drive_link) |

| Late Fusion                                    | 44.5     | 29.9     | 10.8     | [url](https://drive.google.com/drive/folders/1mKt7zKoS6KKzEqKWilHuQtpb36PSztxP?usp=drive_link) |

| Early Fusion                                   | **69.8** | 54.7     | 30.3     | [url](https://drive.google.com/drive/folders/1Ox0Vdh_LPShyK5uGX9s1FHI8USpITy_l?usp=drive_link) |

| [AttFuse](https://arxiv.org/abs/2109.07644)    | 62.7     | 51.6     | 32.1     | [url](https://drive.google.com/drive/folders/1uBTfVMWhbslPzF4f44q36pDHTwEPhoV_?usp=drive_link) |

| [F-Cooper](https://arxiv.org/abs/1909.06459)   | 65.9     | 55.8     | 36.1     | [url](https://drive.google.com/drive/folders/1k677v_DTHXf5lMC9DMBeOLHWdEtd3H-e?usp=drive_link) |

| [Where2Comm](https://arxiv.org/abs/2209.12836) | 67.1     | 55.6     | 34.3     | [url](https://drive.google.com/drive/folders/1aKj5A5wTuy2xJQSiErr0qJ6UWOKxJQFX?usp=drive_link) |

| [CoBEVT](https://arxiv.org/abs/2207.02202)     | 67.6     | **57.2** | **36.2** | [url](https://drive.google.com/drive/folders/1E8CBXLQmBVnShF2TeyTCkPJN_HBGSyzk?usp=drive_link) |

### Results of Cooperative 3D object detection for intersection scenes

| Method                                         | [email protected]   | [email protected]   | [email protected]   | Download Link                                                                                  |

| ---------------------------------------------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- |

| No Fusion                                      | 58.1     | 44.1     | 23.8     | [url](https://drive.google.com/drive/folders/1MMYB9nSBcEprTOIyB3WkJ4nlnUpyEb05?usp=drive_link) |

| Late Fusion                                    | **65.1** | **47.6** | 24.4     | [url](https://drive.google.com/drive/folders/1MMYB9nSBcEprTOIyB3WkJ4nlnUpyEb05?usp=drive_link) |

| Early Fusion                                   | 50.0     | 33.9     | 18.3     | [url](https://drive.google.com/drive/folders/1u8g_f3vzqWqn_D4Sx_UdwX98Jm1wCul_?usp=drive_link) |

| [AttFuse](https://arxiv.org/abs/2109.07644)    | 45.5     | 40.9     | 27.9     | [url](https://drive.google.com/drive/folders/1W6PeNCnsnUMih1qZP3ONMtwcgDiXzciO?usp=drive_link) |

| [F-Cooper](https://arxiv.org/abs/1909.06459)   | 49.5     | 32.0     | 12.9     | [url](https://drive.google.com/drive/folders/1uO7MIHwjXA33CBv861l35IgAL_iMjAon?usp=drive_link) |

| [Where2Comm](https://arxiv.org/abs/2209.12836) | 50.5     | 42.2     | 29.9     | [url](https://drive.google.com/drive/folders/18kcO-G5JHLSj_pd6Gyj5zBYIUK4_fG8k?usp=drive_link) |

| [CoBEVT](https://arxiv.org/abs/2207.02202)     | 53.5     | 45.6     | **32.6** | [url](https://drive.google.com/drive/folders/1jA6Y0cqw4G-CNDCIUkl5QhNSt4axmVmC?usp=drive_link) |

### Results of Cooperative tracking for corridor scenes

| Method       | AMOTA(↑)  | AMOTP(↑)  | sAMOTA(↑) | MOTA(↑)   | MT(↑)     | ML(↓)     |

| ------------ | --------- | --------- | --------- | --------- | --------- | --------- |

| No Fusion    | 8.28      | 22.74     | 34.05     | 23.89     | 17.34     | 42.71     |

| Late Fusion  | 9.60      | 25.77     | 35.64     | 24.75     | 24.37     | 42.96     |

| Early Fusion | **23.78** | **38.18** | 59.16     | 44.30     | **53.02** | **12.81** |

| AttFuse      | 21.75     | 35.31     | 57.43     | 44.50     | 45.73     | 22.86     |

| F-Cooper     | 22.47     | 35.54     | 58.49     | 45.94     | 47.74     | 22.11     |

| Where2Comm   | 22.55     | 36.21     | **59.60** | 46.11     | 50.00     | 19.60     |

| CoBEVT       | 21.54     | 35.69     | 53.85     | **47.32** | 47.24     | 18.09     |

### Results of Cooperative tracking for corridor scenes

| Method       | AMOTA(↑)  | AMOTP(↑)  | sAMOTA(↑) | MOTA(↑)   | MT(↑)     | ML(↓)     |

| ------------ | --------- | --------- | --------- | --------- | --------- | --------- |

| No Fusion    | 18.11     | 39.71     | 58.29     | 49.16     | 35.32     | 41.64     |

| Late Fusion  | **21.57** | 43.40     | **63.02** | **50.58** | **42.75** | **34.20** |

| Early Fusion | 21.38     | **47.71** | 62.93     | 50.15     | 36.80     | 42.75     |

| AttFuse      | 11.84     | 36.63     | 46.92     | 39.32     | 29.00     | 53.90     |

| F-Cooper     | -4.86     | 14.71     | 0.00      | -45.66    | 11.52     | 50.56     |

| Where2Comm   | 14.21     | 38.48     | 50.97     | 42.27     | 29.00     | 45.72     |

| CoBEVT       | 14.82     | 38.71     | 49.04     | 44.67     | 33.83     | 35.69     |

## Citation

If you find RCooper useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

```shell

@inproceedings{hao2024rcooper,

  title={RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception},

  author={Hao, Ruiyang and Fan, Siqi and Dai, Yingru and Zhang, Zhenlin and Li, Chenxi and Wang, Yuntian and Yu, Haibao and Yang, Wenxian and Jirui, Yuan and Nie, Zaiqing},

  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

  year={2024},

  pages={22347-22357}

}

```

## Acknowledgment

- [V2V4Real](https://github.com/ucla-mobility/V2V4Real)

- [OpenCOOD](https://github.com/DerrickXuNu/OpenCOOD)

- [AB3DMOT](https://github.com/xinshuoweng/AB3DMOT)

- [DAIR-V2X](https://github.com/AIR-THU/DAIR-V2X)

Sincere appreciation for their great contributions.