Skip to main content

BEVFormer: Learning Bird’s-Eye-View Representation from Multi-camera Images via Spatiotemporal Transformers

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13669))

Included in the following conference series:

Abstract

3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we 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, we design spatial cross-attention that each BEV query extracts the spatial features from the regions of interest across camera views. For temporal information, we propose temporal self-attention to recurrently fuse the history BEV information. Our 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. The code is available at https://github.com/zhiqi-li/BEVFormer.

Z. Li, W. Wang and H. Li—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Brazil, G., Liu, X.: M3D-RPN: monocular 3D region proposal network for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9287–9296 (2019)

    Google Scholar 

  2. Brazil, G., Pons-Moll, G., Liu, X., Schiele, B.: Kinematic 3D object detection in monocular video. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 135–152. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58592-1_9

    Chapter  Google Scholar 

  3. Bruls, T., Porav, H., Kunze, L., Newman, P.: The right (angled) perspective: improving the understanding of road scenes using boosted inverse perspective mapping. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 302–309. IEEE (2019)

    Google Scholar 

  4. Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621–11631 (2020)

    Google Scholar 

  5. Can, Y.B., Liniger, A., Paudel, D.P., Van Gool, L.: Structured bird’s-eye-view traffic scene understanding from onboard images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15661–15670 (2021)

    Google Scholar 

  6. Can, Y.B., Liniger, A., Unal, O., Paudel, D., Van Gool, L.: Understanding bird’s-eye view semantic HD-maps using an onboard monocular camera. arXiv preprint arXiv:2012.03040 (2020)

  7. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

    Chapter  Google Scholar 

  8. Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1907–1915 (2017)

    Google Scholar 

  9. Chitta, K., Prakash, A., Geiger, A.: Neat: neural attention fields for end-to-end autonomous driving. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15793–15803 (2021)

    Google Scholar 

  10. Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)

  11. Crawshaw, M.: Multi-task learning with deep neural networks: a survey. arXiv preprint arXiv:2009.09796 (2020)

  12. Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764–773 (2017)

    Google Scholar 

  13. Fifty, C., Amid, E., Zhao, Z., Yu, T., Anil, R., Finn, C.: Efficiently identifying task groupings for multi-task learning. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  14. Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. In: International Conference on Machine Learning, pp. 1243–1252. PMLR (2017)

    Google Scholar 

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  16. Hendy, N., et al.: Fishing net: future inference of semantic heatmaps in grids. arXiv preprint arXiv:2006.09917 (2020)

  17. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  18. Hu, A., et al.: Fiery: future instance prediction in bird’s-eye view from surround monocular cameras. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15273–15282 (2021)

    Google Scholar 

  19. Kang, K., Ouyang, W., Li, H., Wang, X.: Object detection from video tubelets with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 817–825 (2016)

    Google Scholar 

  20. Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: fast encoders for object detection from point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12697–12705 (2019)

    Google Scholar 

  21. Lee, Y., Hwang, J.W., Lee, S., Bae, Y., Park, J.: An energy and GPU-computation efficient backbone network for real-time object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  22. Li, Z., et al.: Panoptic segformer: delving deeper into panoptic segmentation with transformers. arXiv preprint arXiv:2109.03814 (2021)

  23. Lin, T.Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936–944 (2017)

    Google Scholar 

  24. Luo, W., Yang, B., Urtasun, R.: Fast and furious: real time end-to-end 3D detection, tracking and motion forecasting with a single convolutional net. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3569–3577 (2018)

    Google Scholar 

  25. Ma, X., Ouyang, W., Simonelli, A., Ricci, E.: 3D object detection from images for autonomous driving: a survey. arXiv preprint arXiv:2202.02980 (2022)

  26. Mousavian, A., Anguelov, D., Flynn, J., Kosecka, J.: 3D bounding box estimation using deep learning and geometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7074–7082 (2017)

    Google Scholar 

  27. Ng, M.H., Radia, K., Chen, J., Wang, D., Gog, I., Gonzalez, J.E.: BEV-seg: bird’s eye view semantic segmentation using geometry and semantic point cloud. arXiv preprint arXiv:2006.11436 (2020)

  28. Pan, B., Sun, J., Leung, H.Y.T., Andonian, A., Zhou, B.: Cross-view semantic segmentation for sensing surroundings. IEEE Robot. Autom. Lett. 5(3), 4867–4873 (2020)

    Article  Google Scholar 

  29. Park, D., Ambrus, R., Guizilini, V., Li, J., Gaidon, A.: Is pseudo-lidar needed for monocular 3D object detection? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3142–3152 (2021)

    Google Scholar 

  30. Philion, J., Fidler, S.: Lift, splat, shoot: encoding images from arbitrary camera rigs by implicitly unprojecting to 3D. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 194–210. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_12

    Chapter  Google Scholar 

  31. Qi, C.R., et al.: Offboard 3D object detection from point cloud sequences. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6134–6144 (2021)

    Google Scholar 

  32. Reading, C., Harakeh, A., Chae, J., Waslander, S.L.: Categorical depth distribution network for monocular 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8555–8564 (2021)

    Google Scholar 

  33. Reiher, L., Lampe, B., Eckstein, L.: A Sim2Real deep learning approach for the transformation of images from multiple vehicle-mounted cameras to a semantically segmented image in bird’s eye view. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–7. IEEE (2020)

    Google Scholar 

  34. Roddick, T., Kendall, A., Cipolla, R.: Orthographic feature transform for monocular 3D object detection. In: BMVC (2019)

    Google Scholar 

  35. Rukhovich, D., Vorontsova, A., Konushin, A.: Imvoxelnet: image to voxels projection for monocular and multi-view general-purpose 3D object detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2397–2406 (2022)

    Google Scholar 

  36. Saha, A., Maldonado, O.M., Russell, C., Bowden, R.: Translating images into maps. arXiv preprint arXiv:2110.00966 (2021)

  37. Simonelli, A., Bulo, S.R., Porzi, L., Lopez-Antequera, M., Kontschieder, P.: Disentangling monocular 3D object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019

    Google Scholar 

  38. Sun, P., et al.: Scalability in perception for autonomous driving: waymo open dataset. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2446–2454 (2020)

    Google Scholar 

  39. Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019)

    Google Scholar 

  40. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  41. Vora, S., Lang, A.H., Helou, B., Beijbom, O.: Pointpainting: sequential fusion for 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4604–4612 (2020)

    Google Scholar 

  42. Wang, T., Xinge, Z., Pang, J., Lin, D.: Probabilistic and geometric depth: detecting objects in perspective. In: Conference on Robot Learning, pp. 1475–1485. PMLR (2022)

    Google Scholar 

  43. Wang, T., Zhu, X., Pang, J., Lin, D.: FCOS3D: fully convolutional one-stage monocular 3D object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 913–922 (2021)

    Google Scholar 

  44. Wang, Y., Chao, W.L., Garg, D., Hariharan, B., Campbell, M., Weinberger, K.Q.: Pseudo-lidar from visual depth estimation: bridging the gap in 3D object detection for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8445–8453 (2019)

    Google Scholar 

  45. Wang, Y., Guizilini, V.C., Zhang, T., Wang, Y., Zhao, H., Solomon, J.: DETR3D: 3D object detection from multi-view images via 3D-to-2D queries. In: Conference on Robot Learning, pp. 180–191. PMLR (2022)

    Google Scholar 

  46. Xie, E., et al.: M\(\hat{}\)2BEV: multi-camera joint 3D detection and segmentation with unified birds-eye view representation. arXiv preprint arXiv:2204.05088 (2022)

  47. Xu, B., Chen, Z.: Multi-level fusion based 3D object detection from monocular images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2345–2353 (2018)

    Google Scholar 

  48. Yan, Y., Mao, Y., Li, B.: Second: sparsely embedded convolutional detection. Sensors 18(10), 3337 (2018)

    Google Scholar 

  49. Yang, W., et al.: Projecting your view attentively: monocular road scene layout estimation via cross-view transformation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15536–15545 (2021)

    Google Scholar 

  50. Yin, T., Zhou, X., Krahenbuhl, P.: Center-based 3D object detection and tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11784–11793 (2021)

    Google Scholar 

  51. Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)

  52. Zhou, Y., Tuzel, O.: Voxelnet: end-to-end learning for point cloud based 3D object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4490–4499 (2018)

    Google Scholar 

  53. Zhu, X., Ma, Y., Wang, T., Xu, Y., Shi, J., Lin, D.: SSN: shape signature networks for multi-class object detection from point clouds. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 581–597. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_35

    Chapter  Google Scholar 

  54. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: deformable transformers for end-to-end object detection. In: International Conference on Learning Representations (2020)

    Google Scholar 

  55. Zhu, X., Xiong, Y., Dai, J., Yuan, L., Wei, Y.: Deep feature flow for video recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2349–2358 (2017)

    Google Scholar 

Download references

Acknowledgement

This work is supported by the Natural Science Foundation of China under Grant 61672273 and Grant 61832008, the Shanghai Committee of Science and Technology (Grant No. 21DZ1100100) and Shanghai AI Laboratory. This work is done when Zhiqi Li is an intern at Shanghai AI Lab.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jifeng Dai .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 2584 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Z. et al. (2022). BEVFormer: Learning Bird’s-Eye-View Representation from Multi-camera Images via Spatiotemporal Transformers. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13669. Springer, Cham. https://doi.org/10.1007/978-3-031-20077-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20077-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20076-2

  • Online ISBN: 978-3-031-20077-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics