Introduction

This repository is an official implementation of StreamPETR.

News

• [2023/07/14] StreamPETR is accepted by ICCV 2023.
• [2023/05/03] StreamPETR-Large is the first online multi-view method that achieves comparable performance (62.0 mAP, 67.6 NDS and 65.3 AMOTA) with the baseline of lidar-based method.

Getting Started

Please follow our documentation step by step. If you like our work, please recommend it to your colleagues and friends.

1. Environment Setup.
2. Data Preparation.
3. Training and Inference.

Model Zoo

Results on NuScenes Val Set.

Model	Setting	Pretrain	Lr Schd	Training Time	NDS	mAP	FPS-pytorch	Config	Download
RepDETR3D	EVA02-L - 900q	EVA02-L	24ep	12 hours (A100)	60.8	52.1	-	config	model
StreamPETR	V2-99 - 900q	FCOS3D	24ep	13 hours	57.1	48.2	12.5	config	model/log
RepDETR3D	V2-99 - 900q	FCOS3D	24ep	13 hours	58.4	50.1	13.1	config	model/log
StreamPETR	R50 - 900q	ImageNet	90ep	36 hours	53.7	43.2	26.7	config	model/log
StreamPETR	R50 - 428q	NuImg	60ep	26 hours	54.6	44.9	31.7	config	model/log

The detailed results can be found in the training log. For other results on nuScenes val set, please see Here. Notes:

• FPS is measured on NVIDIA RTX 3090 GPU with batch size of 1 (containing 6 view images, without using flash attention) and FP32.
• The training time is measured with 8x 2080ti GPUs.
• RepDETR3D uses deformable attention, which is inspired by DETR3D and Sparse4D.

Results on NuScenes Test Set.

Model	Setting	Pretrain	NDS	mAP	AMOTA	AMOTP
StreamPETR	V2-99 - 900q	DD3D	63.6	55.0	-	-
StreamPETR	ViT-Large-900q	-	67.6	62.0	65.3	87.6

Currently Supported Features

• StreamPETR code (also including PETR and Focal-PETR)
• Flash attention
• Deformable attention (RepDETR3D)
• Checkpoints
• Sliding window training
• Efficient training in streaming video
• TensorRT inference
• 3D object tracking

Acknowledgements

We thank these great works and open-source codebases:

• 3D Detection. MMDetection3d, DETR3D, PETR, BEVFormer, SOLOFusion, Sparse4D.
• Multi-object tracking. MOTR, PF-Track.

Citation

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

@article{wang2023exploring,
  title={Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection},
  author={Wang, Shihao and Liu, Yingfei and Wang, Tiancai and Li, Ying and Zhang, Xiangyu},
  journal={arXiv preprint arXiv:2303.11926},
  year={2023}
}