wangjiongw / BEVFormer

This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.

Home Page:https://arxiv.org/abs/2203.17270

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BEVFormer: a Cutting-edge Baseline for Camera-based Detection

BEVFormer.mp4

BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

  • Paper | Blog (in Chinese) | Presentation Slides at CVPR 2022 Workshop (soon) | Live-streaming video on BEV Perception (soon)

News

  • [2022/6/16]: We added two BEVformer configurations, which require less GPU memory than the base version. Please pull this repo to obtain the latest codes.
  • [2022/6/13]: We release an initial version of BEVFormer. It achieves a baseline result of 51.7% NDS on nuScenes.
  • [2022/5/23]: 🚀🚀Built on top of BEVFormer, BEVFormer++, gathering up all best practices in recent SOTAs and our unique modification, ranks 1st on Waymo Open Datast 3D Camera-Only Detection Challenge. We will present BEVFormer++ on CVPR 2022 Autonomous Driving Workshop.
  • [2022/3/10]: 🚀BEVFormer achieve the SOTA on nuScenes Detection Task with 56.9% NDS (camera-only)!

Abstract

In this work, the authors present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries. To aggregate spatial information, the authors design a spatial cross-attention that each BEV query extracts the spatial features from the regions of interest across camera views. For temporal information, the authors propose a temporal self-attention to recurrently fuse the history BEV information. The proposed approach achieves the new state-of-the-art 56.9% in terms of NDS metric on the nuScenes test set, which is 9.0 points higher than previous best arts and on par with the performance of LiDAR-based baselines.

Methods

method

Getting Started

Model Zoo

Backbone Method Lr Schd NDS mAP memroy Config Download
R50 BEVFormer-tiny_fp16 24ep 35.9 25.7 - config modle/log
R50 BEVFormer-tiny 24ep 35.4 25.2 6500M config modle/log
R101-DCN BEVFormer-small 24ep 47.9 37.0 10500M config model/log
R101-DCN BEVFormer-base 24ep 51.7 41.6 28500M config model/log

Catalog

  • BEV Segmentation checkpoints
  • BEV Segmentation code
  • 3D Detection checkpoints
  • 3D Detection code
  • Initialization

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{li2022bevformer,
  title={BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers},
  author={Li, Zhiqi and Wang, Wenhai and Li, Hongyang and Xie, Enze and Sima, Chonghao and Lu, Tong and Qiao, Yu and Dai, Jifeng}
  journal={arXiv preprint arXiv:2203.17270},
  year={2022}
}

Acknowledgement

Many thanks to these excellent open source projects:

↳ Stargazers

Stargazers repo roster for @nastyox/Repo-Roster

↳ Forkers

Forkers repo roster for @nastyox/Repo-Roster

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About

This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.

https://arxiv.org/abs/2203.17270

License:Apache License 2.0


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