OpenCOOD is an Open COOperative Detection framework for autonomous driving. It is also the official implementation of the ICRA 2022 paper OPV2V.

News:

• 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 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:

  • OPV2V [ICRA2022]
  • V2XSet [ECCV2022]

• Provide multiple SOTA 3D detection backbone:

  • PointPillar
  • Pixor
  • VoxelNet
  • SECOND

• Support SOTA multi-agent perception models:

  • Attentive Fusion [ICRA2022]
  • Cooper [ICDCS]
  • F-Cooper [SEC2019]
  • V2VNet [ECCV2022]
  • FPV-RCNN [RAL2022]
  • DiscoNet [NeurIPS2022]
  • V2X-ViT [ECCV2022]

• Provide a convenient log replay toolbox for OPV2V dataset. Check here to see more details.

Data Downloading

All the data can be downloaded from google drive. 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 fiels, 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:

cat train.zip.parta* > train.zip
unzip train.zip

Installation

Please refer to data introduction and installation guide to prepare data and install OpenCOOD. To see more details of OPV2V data, please check our website.

Quick Start

Data sequence visualization

To quickly visualize the LiDAR stream in the OPV2V dataset, first modify the validate_dir in your opencood/hypes_yaml/visualization.yaml to the opv2v data path on your local machine, e.g. opv2v/validate, and the run the following commond:

cd ~/OpenCOOD
python opencood/visualization/vis_data_sequence.py [--color_mode ${COLOR_RENDERING_MODE}]

Arguments Explanation:

• color_mode : str type, indicating the lidar color rendering mode. You can choose from 'constant', 'intensity' or 'z-value'.

Train your model

OpenCOOD uses yaml file to configure all the parameters for training. To train your own model from scratch or a continued checkpoint, run the following commonds:

python opencood/tools/train.py --hypes_yaml ${CONFIG_FILE} [--model_dir  ${CHECKPOINT_FOLDER}]

Arguments Explanation:

• hypes_yaml: the path of the training configuration file, e.g. opencood/hypes_yaml/second_early_fusion.yaml, meaning you want to train an early fusion model which utilizes SECOND as the backbone. See Tutorial 1: Config System to learn more about the rules of the yaml files.
• model_dir (optional) : the path of the checkpoints. This is used to fine-tune the trained models. When the model_dir is given, the trainer will discard the hypes_yaml and load the config.yaml in the checkpoint folder.

Test the model

Before you run the following command, first make sure the validation_dir in config.yaml under your checkpoint folder refers to the testing dataset path, e.g. opv2v_data_dumping/test.

python opencood/tools/inference.py --model_dir ${CHECKPOINT_FOLDER} --fusion_method ${FUSION_STRATEGY} [--show_vis] [--show_sequence]

Arguments Explanation:

• model_dir: the path to your saved model.
• fusion_method: indicate the fusion strategy, currently support 'early', 'late', and 'intermediate'.
• show_vis: whether to visualize the detection overlay with point cloud.
• show_sequence : the detection results will visualized in a video stream. It can NOT be set with show_vis at the same time.

The evaluation results will be dumped in the model directory.

Benchmark and model zoo

Results on OPV2V LiDAR-track (AP@0.7 for no-compression/ compression)

	Backbone	Fusion Strategy	Bandwidth (Megabit), before/after compression	Default Towns	Culver City	Download
Naive Late	PointPillar	Late	0.024/0.024	0.781/0.781	0.668/0.668	url
Cooper	PointPillar	Early	7.68/7.68	0.800/x	0.696/x	url
Attentive Fusion	PointPillar	Intermediate	126.8/1.98	0.815/0.810	0.735/0.731	url
F-Cooper	PointPillar	Intermediate	72.08/1.12	0.790/0.788	0.728/0.726	url
V2VNet	PointPillar	Intermediate	72.08/1.12	0.822/0.814	0.734/0.729	url
FPV-RCNN	PV-RCNN	Intermediate(2 stage)	0.24/0.24	0.820/0.820	0.763/0.763	url
Naive Late	VoxelNet	Late	0.024/0.024	0.738/0.738	0.588/0.588	url
Cooper	VoxelNet	Early	7.68/7.68	0.758/x	0.677/x	url
Attentive Fusion	VoxelNet	Intermediate	576.71/1.12	0.864/0.852	0.775/0.746	url
Naive Late	SECOND	Late	0.024/0.024	0.775/0.775	0.682/0.682	url
Cooper	SECOND	Early	7.68/7.68	0.813/x	0.738/x	url
Attentive	SECOND	Intermediate	63.4/0.99	0.826/0.783	0.760/0.760	url
Naive Late	PIXOR	Late	0.024/0.024	0.578/0.578	0.360/0.360	url
Cooper	PIXOR	Early	7.68/7.68	0.678/x	0.558/x	url
Attentive	PIXOR	Intermediate	313.75/1.22	0.687/0.612	0.546/0.492	url

Note:

• We suggest using PointPillar as the backbone when you are creating your method and try to compare with our benchmark, as we implement most of the SOTA methods with this backbone only.
• We assume the transimssion rate is 27Mbp/s. Considering the frequency of LiDAR is 10Hz, the bandwidth requirement should be less than 2.7Mbp to avoid severe delay.
• A 'x' in the benchmark table represents the bandwidth requirement is too large, which can not be considered to employ in practice.

Results of BEV semantic segmentation on OPV2V camera-track (IoU)

	Backbone	Fusion Strategy	Vehicles	Road Surface	Lane	Download
No Fusion	CVT	No Fusion	37.7	57.8	43.7	None
Map Fusion	CVT	Late	45.1	60.0	44.1	None
Attentive Fusion	CVT	Intermediate	51.9	60.5	46.2	None
F-Cooper	CVT	Intermediate	52.5	60.4	46.5	None
V2VNet	CVT	Intermediate	53.5	60.2	47.5	None
DiscoNet	CVT	Intermediate	52.9	60.7	45.8	None
FuseBEVT	CVT	Intermediate	59.0	62.1	49.2	url
CoBEVT	SinBEVT	Intermediate	60.4	63.0	53.0	url

Note: To play with OPV2V camera data, please check here: https://github.com/DerrickXuNu/CoBEVT

Tutorials

We have a series of tutorials to help you understand OpenCOOD more. Please check the series of our tutorials.

Citation

If you are using our OpenCOOD framework or OPV2V dataset for your research, please cite the following paper:

@inproceedings{xu2022opencood,
 author = {Runsheng Xu, Hao Xiang, Xin Xia, Xu Han, Jinlong Li, Jiaqi Ma},
 title = {OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication},
 booktitle = {2022 IEEE International Conference on Robotics and Automation (ICRA)},
 year = {2022}}

Supported Projects

OpenCOOD has supported several top conference papers in cooperative perception field.

Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
Yue Hu, Shaoheng Fang, Zixing Lei, Yiqi Zhong, Siheng Chen
Neurips 2022
[Paper] [Code]

Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds
Donghao Qiao, Farhana Zulkernine
WACV 2023
[Paper]

CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers
Runsheng Xu*, Zhengzhong Tu*, Hao Xiang, Wei Shao, Bolei Zhou, Jiaqi Ma
CoRL2022
[Paper] [Code]

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer
Runsheng Xu*, Hao Xiang*, Zhengzhong Tu*, Xin Xia, Ming-Hsuan Yang, Jiaqi Ma
ECCV2022
[Paper] [Code] [Talk]

OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication
Runsheng Xu*, Hao Xiang*, Xin Xia, Jinlong Li, Jiaqi Ma
ICRA2022
[Paper] [Website] [Code]