[NeurIPS 2024] UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles

website | paper

UAV3D is a public large-scale benchmark designed for 3D perception tasks from Unmanned Aerial Vehicle (UAV) platforms. This benchmark comprises the synthetic data and 3D perception algorithms, aiming to facilitate research in both single UAV and collaborative UAVs 3D perception tasks.

News

• (2024/9) The paper got accepted at NeurIPS 2024.
• (2024/9) UAV3D V1.0-mini (Google Drive or Baidu Netdisk) is released.
• (2024/6) Source code and pre-trained models are released.
• (2024/6) UAV3D V1.0 (Google Drive or Baidu Netdisk) is released.

Installation

• Installation

Prepare dataset

• Prepare dataset

Training and evaluation

• Training and evaluation

Main Results

3D Object Detection (UAV3D val)

Model	Backbone	Size	mAP↑	NDS↑	mATE↓	mASE↓	mAOE↓	Checkpoint	Log
PETR	Res-50	704×256	0.512	0.571	0.741	0.173	0.072	link	link
BEVFusion	Res-50	704×256	0.487	0.458	0.615	0.152	1.000	link	link
DETR3D	Res-50	704×256	0.430	0.509	0.791	0.187	0.100	link	link
PETR	Res-50	800×450	0.581	0.632	0.625	0.160	0.064	link	link
BEVFusion	Res-101	800×450	0.536	0.582	0.521	0.154	0.343	link	link
DETR3D	Res-101	800×450	0.618	0.671	0.494	0.158	0.070	link	link

3D Object Tracking (UAV3D val)

Model	Backbone	Size	AMOTA↑	AMOTP↓	MOTA↑	MOTP↓	TID↓	LGD↓	det_result	Log
PETR	Res-50	704×256	0.199	1.294	0.195	0.794	1.280	2.970	link	link
BEVFusion	Res-50	704×256	0.566	1.137	0.501	0.695	0.790	1.600	link	link
DETR3D	Res-50	704×256	0.089	1.382	0.121	0.800	1.540	3.530	link	link
PETR	Res-50	800×450	0.291	1.156	0.256	0.677	1.090	2.550	link	link
BEVFusion	Res-101	800×450	0.606	1.006	0.540	0.627	0.700	1.390	link	link
DETR3D	Res-101	800×450	0.262	1.123	0.238	0.561	1.140	2.720	link	link

Collaborative 3D Object Detection (UAV3D val)

Model	mAP↑	NDS↑	mATE↓	mASE↓	mAOE↓	AP@IoU=0.5↑	AP@IoU=0.7↑	Checkpoint	Log
Lower-bound	0.544	0.556	0.540	0.147	0.578	0.457	0.140	link	link
When2com	0.550	0.507	0.534	0.156	0.679	0.461	0.166	link	link
Who2com	0.546	0.597	0.541	0.150	0.263	0.453	0.141	link	link
V2VNet	0.647	0.628	0.508	0.167	0.533	0.545	0.141	link	link
DiscoNet	0.700	0.689	0.423	0.143	0.422	0.649	0.247	link	link
Upper-bound	0.720	0.748	0.391	0.106	0.117	0.673	0.316	link	link

Collaborative 3D Object Tracking (UAV3D val)

Model	AMOTA↑	AMOTP↓	MOTA↑	MOTP↓	TID↓	LGD↓	det_result	Log
Lower-bound	0.644	1.018	0.593	0.611	0.620	1.280	link	link
When2com	0.646	1.012	0.595	0.618	0.590	1.200	link	link
Who2com	0.648	1.012	0.602	0.623	0.580	1.200	link	link
V2VNet	0.782	0.803	0.735	0.587	0.360	0.710	link	link
DiscoNet	0.809	0.703	0.766	0.516	0.300	0.590	link	link
Upper-bound	0.812	0.672	0.781	0.476	0.300	0.570	link	link

Citation

If you find this repository useful, please consider giving a star ⭐ and citation 📘:

@inproceedings{uav3d2024,
  title={UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles},
  author={Hui Ye and Raj Sunderraman and Shihao Ji},
  booktitle={The 38th Conference on Neural Information Processing Systems (NeurIPS)},
  year={2024}
}

Acknowledgement

In collecting UAV3D, we received valuable help and suggestions from the authors of CoPerception-UAV and Where2comm.

For 3D object detection task, our implementation is based on PETR, BEVFusion, and DETR3D.

For Collaborative 3D object detection task, our implementation is based on BEVFusion and CoPerception.

For object tracking task, our implementation is based on CenterPoint.

The software and data were created by Georgia State University Research Foundation under Army Research Laboratory (ARL) Award Numbers W911NF-22-2-0025 and W911NF-23-2-0224. ARL, as the Federal awarding agency, reserves a royalty-free, nonexclusive and irrevocable right to reproduce, publish, or otherwise use this software for Federal purposes, and to authorize others to do so in accordance with 2 CFR 200.315(b).