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[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.
| Date | Stars |
|---|---|
| 2026-07-24 | 825 |
| 2026-07-25 | 825 |
| 2026-07-28 | 825 |
| 2026-07-30 | 825 |
| 2026-08-06 | 825 |
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<div align="center">
<img src="images/opencood.png" width="600"/>
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[](https://arxiv.org/pdf/2109.07644.pdf)
[](https://opencood.readthedocs.io/en/latest/?badge=latest)
[](https://opensource.org/licenses/MIT)
OpenCOOD is an <strong>Open</strong> <strong>COO</strong>perative <strong>D</strong>etection framework for autonomous driving. It is also the official implementation of the <strong> ICRA 2022 </strong>
paper [OPV2V.](https://arxiv.org/abs/2109.07644)
<p align="center">
<img src="images/demo1.gif" width="600" alt="" class="img-responsive">
<img src="images/camera_demo.gif" width="600" alt="" class="img-responsive">
</p>
## News:
- 12/28/2022: OpenCOOD now support multi-gpu training.
- 12/21/2022: V2XSet (ECCV2022) is supported by OpenCOOD now!
- 12/16/2022: Both spconv 1.2.1 and spconv 2.x are supported!
- 12/04/2022: The log replay tool for OPV2V is online now! With
this toolbox, you can 100% replay all the events in the offline dataset and add/change any sensors/groundtruth you
want to explore the tasks that the origin dataset do not support. Check [here](logreplay/README.md) to see more details.
- 09/15/2022: So far OpenCOOD has supported several top conference papers, including ECCV,ICRA,CoRL,NeurIPS,WACV! The bottom of this project page lists the detailed information.
## Features
- Provide easy data API for multiple popular multi-agent perception dataset:
- [x] [OPV2V [ICRA2022]](https://mobility-lab.seas.ucla.edu/opv2v/)
- [x] [V2XSet [ECCV2022]](https://arxiv.org/pdf/2203.10638.pdf)
- [ ] [DAIR-V2X [CVPR2022]](https://arxiv.org/abs/2204.05575)
- [ ] [V2V4Real [CVPR2023 Highlight]](https://arxiv.org/abs/2303.07601)
- Provide APIs to allow users use different sensor modalities
- [x] LiDAR APIs
- [ ] Camera APIs
- [ ] Radar APIs
- Provide multiple SOTA 3D detection backbone:
- [X] [PointPillar](https://arxiv.org/abs/1812.05784)
- [X] [Pixor](https://arxiv.org/abs/1902.06326)
- [X] [VoxelNet](https://arxiv.org/abs/1711.06396)
- [X] [SECOND](https://www.mdpi.com/1424-8220/18/10/3337)
- Support multiple sparse convolution versions
- [X] Spconv 1.2.1
- [X] Spconv 2.x
- Support SOTA multi-agent perception models:
- [x] [Attentive Fusion [ICRA2022]](https://arxiv.org/abs/2109.07644)
- [x] [Cooper [ICDCS]](https://arxiv.org/abs/1905.05265)
- [x] [F-Cooper [SEC2019]](https://arxiv.org/abs/1909.06459)
- [x] [V2VNet [ECCV2022]](https://arxiv.org/abs/2008.07519)
- [x] [CoAlign (fusion only) [ICRA2023]](https://arxiv.org/abs/2211.07214)
- [x] [FPV-RCNN [RAL2022]](https://arxiv.org/pdf/2109.11615.pdf)
- [ ] [DiscoNet [NeurIPS2021]](https://arxiv.org/abs/2111.00643)
- [x] [V2X-ViT [ECCV2022]](https://github.com/DerrickXuNu/v2x-vit)
- [x] [CoBEVT [CoRL2022]](https://arxiv.org/abs/2207.02202)
- [ ] [AdaFusion [WACV2023]](https://arxiv.org/abs/2208.00116)
- [x] [Where2comm [NeurIPS2022]](https://arxiv.org/abs/2209.12836)
- [x] [V2VAM [TIV2023]](https://arxiv.org/abs/2212.08273)
- **Provide a convenient log replay toolbox for OPV2V dataset.** Check [here](logreplay/README.md) to see more details.
## Data Downloading
All the data can be downloaded from [UCLA BOX](https://ucla.app.box.com/v/UCLA-MobilityLab-OPV2V). If you have a good internet, you can directly
download the complete large zip file such as `train.zip`. In case you suffer from downloading large files, we also split each data set into small chunks, which can be found
in the directory ending with `_chunks`, such as `train_chunks`. After downloading, please run the following command to each set to merge those chunks together:
```python
cat train.zip.part* > train.zip
unzip train.zip
```
## Installation
Please refer tExcerpt of 20,721 characters
Read on GitHub
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Shenyuan Gao · NVIDIA GEAR
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:277a5d526f06a970, topic:deep-learning, topic:pytorch
matched fp:277a5d526f06a970, topic:computer-vision
matched fp:277a5d526f06a970, topic:multi-agent-systems, readme:multi-agent, readme:multi agent
matched fp:277a5d526f06a970, topic:autonomous-driving, readme:autonomous driving