Skip to main content
SpringerLink
Log in

Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models

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

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13697))

Included in the following conference series:

Abstract

3D point-clouds and 2D images are different visual representations of the physical world. While human vision can understand both representations, computer vision models designed for 2D image and 3D point-cloud understanding are quite different. Our paper explores the potential of transferring 2D model architectures and weights to understand 3D point-clouds, by empirically investigating the feasibility of the transfer, the benefits of the transfer, and shedding light on why the transfer works. We discover that we can indeed use the same architecture and pretrained weights of a neural net model to understand both images and point-clouds. Specifically, we transfer the image-pretrained model to a point-cloud model by copying or inflating the weights. We find that finetuning the transformed image-pretrained models (FIP) with minimal efforts—only on input, output, and normalization layers—can achieve competitive performance on 3D point-cloud classification, beating a wide range of point-cloud models that adopt task-specific architectures and use a variety of tricks. When finetuning the whole model, the performance gets further improved. Meanwhile, FIP improves data efficiency, reaching up to 10.0 top-1 accuracy percent on few-shot classification. It also speeds up the training of point-cloud models by up to 11.1x for a target accuracy (e.g., 90% accuracy). Lastly, we provide an explanation of the image to point-cloud transfer from the aspect of neural collapse. The code is available at: https://github.com/chenfengxu714/image2point.

C. Xu and S. Yang—Equal contribution.

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

Access this chapter

Log in via an institution
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

References

  1. Armeni, I., Sax, S., Zamir, A.R., Savarese, S.: Joint 2d–3d-semantic data for indoor scene understanding. arXiv preprint arXiv:1702.01105 (2017)

  2. Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. arXiv preprint arXiv:1906.00910 (2019)

  3. Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  4. Ben-david, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: Advances in Neural Information Processing Systems 19, pp. 137–144. Curran Associates, Inc. (2006)

    Google Scholar 

  5. Boulch, A., Le Saux, B., Audebert, N.: Unstructured point cloud semantic labeling using deep segmentation networks. 3DOR 2, 7 (2017)

    Google Scholar 

  6. Caesar, H.,et al.: 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 

  7. Caron, M., Bojanowski, P., Mairal, J., Joulin, A.: Unsupervised pre-training of image features on non-curated data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2959–2968 (2019)

    Google Scholar 

  8. Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. arXiv preprint arXiv:2006.09882 (2020)

  9. Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017)

    Google Scholar 

  10. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning, pp. 1597–1607. PMLR (2020)

    Google Scholar 

  11. Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.: Big self-supervised models are strong semi-supervised learners. arXiv preprint arXiv:2006.10029 (2020)

  12. Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)

  13. Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3075–3084 (2019)

    Google Scholar 

  14. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR), IEEE (2017)

    Google Scholar 

  15. Dai, A., Nießner, M.: 3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 452–468 (2018)

    Google Scholar 

  16. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  17. Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  18. Feng, D., Zhou, Y., Xu, C., Tomizuka, M., Zhan, W.: A simple and efficient multi-task network for 3d object detection and road understanding. arXiv preprint arXiv:2103.04056 (2021)

  19. Galanti, T., György, A., Hutter, M.: On the role of neural collapse in transfer learning. In: International Conference on Learning Representations (2022), https://openreview.net/forum?id=SwIp410B6aQ

  20. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361 (2012)

    Google Scholar 

  21. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580–587 (2014)

    Google Scholar 

  22. Goyal, A., Law, H., Liu, B., Newell, A., Deng, J.: Revisiting point cloud shape classification with a simple and effective baseline. arXiv preprint arXiv:2106.05304 (2021)

  23. Goyal, P., et al.: Self-supervised pretraining of visual features in the wild. arXiv preprint arXiv:2103.01988 (2021)

  24. Gur, S., Wolf, L.: Single image depth estimation trained via depth from defocus cues. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7683–7692 (2019)

    Google Scholar 

  25. Han, X.Y., Papyan, V., Donoho, D.L.: Neural collapse under mse loss: Proximity to and dynamics on the central path (2021)

    Google Scholar 

  26. He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)

    Google Scholar 

  27. 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 

  28. Henaff, O.: Data-efficient image recognition with contrastive predictive coding. In: International Conference on Machine Learning, pp. 4182–4192. PMLR (2020)

    Google Scholar 

  29. Hjelm, R.D., et al.: Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018)

  30. Hou, J., Graham, B., Nießner, M., Xie, S.: Exploring data-efficient 3d scene understanding with contrastive scene contexts. arXiv preprint arXiv:2012.09165 (2020)

  31. Hou, J., Graham, B., Nießner, M., Xie, S.: Exploring data-efficient 3D scene understanding with contrastive scene contexts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15587–15597 (2021)

    Google Scholar 

  32. Hua, B.S., Tran, M.K., Yeung, S.K.: Pointwise convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 984–993 (2018)

    Google Scholar 

  33. Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)

    Google Scholar 

  34. Kataoka, H., et al.: Pre-training without natural images. In: Proceedings of the Asian Conference on Computer Vision (2020)

    Google Scholar 

  35. Klokov, R., Lempitsky, V.: Escape from cells: Deep kd-networks for the recognition of 3d point cloud models. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 863–872 (2017)

    Google Scholar 

  36. Komarichev, A., Zhong, Z., Hua, J.: A-cnn: Annularly convolutional neural networks on point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7421–7430 (2019)

    Google Scholar 

  37. 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 

  38. Lawin, F.J., Danelljan, M., Tosteberg, P., Bhat, G., Khan, F.S., Felsberg, M.: Deep projective 3D semantic segmentation. In: Felsberg, M., Heyden, A., Krüger, N. (eds.) CAIP 2017. LNCS, vol. 10424, pp. 95–107. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64689-3_8

    Chapter  Google Scholar 

  39. Lee, D., et al.: Regularization strategy for point cloud via rigidly mixed sample. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15900–15909 (2021)

    Google Scholar 

  40. Li, J., Chen, B.M., Lee, G.H.: So-net: Self-organizing network for point cloud analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 9397–9406 (2018)

    Google Scholar 

  41. Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: Pointcnn: Convolution on \(\chi \)-transformed points. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. pp. 828–838 (2018)

    Google Scholar 

  42. Liu, Y., Fan, B., Meng, G., Lu, J., Xiang, S., Pan, C.: Densepoint: Learning densely contextual representation for efficient point cloud processing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5239–5248 (2019)

    Google Scholar 

  43. Liu, Y.C., et al.: Learning from 2d: Pixel-to-point knowledge transfer for 3d pretraining. arXiv preprint arXiv:2104.04687 (2021)

  44. Liu, Z., Hu, H., Cao, Y., Zhang, Z., Tong, X.: A closer look at local aggregation operators in point cloud analysis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 326–342. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58592-1_20

    Chapter  Google Scholar 

  45. Liu, Z., Qi, X., Fu, C.W.: 3d-to-2d distillation for indoor scene parsing. arXiv preprint arXiv:2104.02243 (2021)

  46. Lu, K., Grover, A., Abbeel, P., Mordatch, I.: Pretrained transformers as universal computation engines. arXiv preprint arXiv:2103.05247 (2021)

  47. Lu, Y., et al.: Open-vocabulary 3d detection via image-level class and debiased cross-modal contrastive learning. arXiv preprint arXiv:2207.01987 (2022)

  48. Mansour, Y.: Learning and domain adaptation. In: Algorithmic Learning Theory, 20th International Conference, ALT, pp. 4–6 (2009)

    Google Scholar 

  49. Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation: Learning bounds and algorithms. In: COLT - The 22nd Conference on Learning Theory (2009)

    Google Scholar 

  50. Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: Rangenet++: Fast and accurate lidar semantic segmentation. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4213–4220. IEEE (2019)

    Google Scholar 

  51. Papyan, V., Han, X.Y., Donoho, D.L.: Prevalence of neural collapse during the terminal phase of deep learning training. Proc. Natl. Acad. Sci. 117(40), 24652–24663 (2020)

    Article  MathSciNet  Google Scholar 

  52. Park, J., Xu, C., Zhou, Y., Tomizuka, M., Zhan, W.: Detmatch: Two teachers are better than one for joint 2d and 3d semi-supervised object detection. arXiv preprint arXiv:2203.09510 (2022)

  53. Pomerleau, F., Colas, F., Siegwart, R.: A review of point cloud registration algorithms for mobile robotics. Foundations Trends Robot. 4(1), 1–104 (2015)

    Article  Google Scholar 

  54. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation (2016). arxiv:1612.00593

  55. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. arXiv preprint arXiv:1706.02413 (2017)

  56. Qiu, S., Anwar, S., Barnes, N.: Dense-resolution network for point cloud classification and segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3813–3822 (2021)

    Google Scholar 

  57. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)

    Google Scholar 

  58. Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2020)

    Google Scholar 

  59. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  60. Shan, H., Zhang, Y., Yang, Q., Kruger, U., Kalra, M.K., Sun, L., Cong, W., Wang, G.: 3-D convolutional encoder-decoder network for low-dose ct via transfer learning from a 2-d trained network. IEEE Trans. Med. Imaging 37(6), 1522–1534 (2018)

    Article  Google Scholar 

  61. Shi, B., Bai, S., Zhou, Z., Bai, X.: Deeppano: deep panoramic representation for 3-D shape recognition. IEEE Signal Process. Lett. 22(12), 2339–2343 (2015). https://doi.org/10.1109/LSP.2015.2480802

    Article  Google Scholar 

  62. Sketchup: 3d modeling online free|3d warehouse models. https://3dwarehouse.sketchup.com (2021)

  63. Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE international conference on computer vision, pp. 945–953 (2015)

    Google Scholar 

  64. Tang, H., et al.: Searching efficient 3d architectures with sparse point-voxel convolution. In: European Conference on Computer Vision (2020)

    Google Scholar 

  65. Wang, H., Liu, Q., Yue, X., Lasenby, J., Kusner, M.J.: Unsupervised point cloud pre-training via view-point occlusion, completion. arXiv preprint arXiv:2010.01089 (2020)

  66. Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-cnn: Octree-based convolutional neural networks for 3d shape analysis. ACM Trans. Graph. (TOG) 36(4), 1–11 (2017)

    Google Scholar 

  67. Wang, Y., Chao, W.L., Garg, D., Hariharan, B., Campbell, M., Weinberger, K.: Pseudo-lidar from visual depth estimation: Bridging the gap in 3D object detection for autonomous driving. In: CVPR (2019)

    Google Scholar 

  68. Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. Acm Trans. Graph. (tog) 38(5), 1–12 (2019)

    Article  Google Scholar 

  69. Wang, Z., Zhan, W., Tomizuka, M.: Fusing bird’s eye view lidar point cloud and front view camera image for 3D object detection. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1–6. IEEE (2018)

    Google Scholar 

  70. Wu, B., Wan, A., Yue, X., Keutzer, K.: Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3D lidar point cloud. In: ICRA (2018)

    Google Scholar 

  71. Wu, B., Zhou, X., Zhao, S., Yue, X., Keutzer, K.: Squeezesegv 2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud. In: ICRA (2019)

    Google Scholar 

  72. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

  73. Xie, S., Gu, J., Guo, D., Qi, C.R., Guibas, L., Litany, O.: PointContrast: unsupervised pre-training for 3D point cloud understanding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 574–591. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_34

    Chapter  Google Scholar 

  74. Xu, C., et al.: Pretram: Self-supervised pre-training via connecting trajectory and map. arXiv preprint arXiv:2204.10435 (2022)

  75. Xu, C.: SqueezeSegV3: spatially-adaptive convolution for efficient point-cloud segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 1–19. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58604-1_1

    Chapter  Google Scholar 

  76. Xu, C., et al.: You only group once: Efficient point-cloud processing with token representation and relation inference module. arXiv preprint arXiv:2103.09975 (2021)

  77. Xu, X., Lee, G.H.: Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13706–13715 (2020)

    Google Scholar 

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

    Article  Google Scholar 

  79. Yang, B., Luo, W., Urtasun, R.: Pixor: Real-time 3D object detection from point clouds. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 7652–7660 (2018)

    Google Scholar 

  80. Yin, W., Liu, Y., Shen, C.: Virtual normal: Enforcing geometric constraints for accurate and robust depth prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)

    Google Scholar 

  81. Yue, X., Wu, B., Seshia, S.A., Keutzer, K., Sangiovanni-Vincentelli, A.L.: A lidar point cloud generator: from a virtual world to autonomous driving. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, pp. 458–464 (2018)

    Google Scholar 

  82. Zhang, J., et al.: Pointcutmix: Regularization strategy for point cloud classification. arXiv preprint arXiv:2101.01461 (2021)

  83. Zhang, Z., Girdhar, R., Joulin, A., Misra, I.: Self-supervised pretraining of 3d features on any point-cloud. arXiv preprint arXiv:2101.02691 (2021)

  84. Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16259–16268 (2021)

    Google Scholar 

  85. Zhou, H., et al.: Cylinder3d: An effective 3d framework for driving-scene lidar semantic segmentation. arXiv preprint arXiv:2008.01550 (2020)

Download references

Acknowledgements

Co-authors from UC Berkeley were sponsored by Berkeley Deep Drive (BDD). Tomer Galanti’s contribution was supported by the Center for Minds, Brains and Machines (CBMM), funded by NSF STC award CCF-1231216.

Author information

Authors and Affiliations

  1. University of California, Berkeley, Berkeley, USA

    Chenfeng Xu, Shijia Yang, Xiangyu Yue, Bohan Zhai, Wei Zhan, Kurt Keutzer & Masayoshi Tomizuka

  2. Massachusetts Institute of Technology, Cambridge, USA

    Tomer Galanti

  3. Meta Reality Labs, Menlo Park, USA

    Bichen Wu & Peter Vajda

Authors
  1. Chenfeng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shijia Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tomer Galanti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bichen Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiangyu Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bohan Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wei Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Peter Vajda
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kurt Keutzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Masayoshi Tomizuka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bichen Wu .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 620 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

Xu, C. et al. (2022). Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models. 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 13697. Springer, Cham. https://doi.org/10.1007/978-3-031-19836-6_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19836-6_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19835-9

  • Online ISBN: 978-3-031-19836-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation