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DetMatch: Two Teachers are Better than One for Joint 2D and 3D Semi-Supervised Object Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion. Current methods develop independent pipelines for 2D and 3D semi-supervised learning despite the availability of paired image and point cloud frames. Observing that the distinct characteristics of each sensor cause them to be biased towards detecting different objects, we propose DetMatch, a flexible framework for joint semi-supervised learning on 2D and 3D modalities. By identifying objects detected in both sensors, our pipeline generates a cleaner, more robust set of pseudo-labels that both demonstrates stronger performance and stymies single-modality error propagation. Further, we leverage the richer semantics of RGB images to rectify incorrect 3D class predictions and improve localization of 3D boxes. Evaluating our method on the challenging KITTI and Waymo datasets, we improve upon strong semi-supervised learning methods and observe higher quality pseudo-labels. Code will be released here: https://github.com/Divadi/DetMatch.

J. Park—Work conducted during visit to University of California, Berkeley.

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References

  1. Bachman, P., Alsharif, O., Precup, D.: Learning with pseudo-ensembles. Adv. Neural Inf. Proc. Syst. 27 (2014)

    Google Scholar 

  2. Berthelot, D., et al.: Remixmatch: semi-supervised learning with distribution matching and augmentation anchoring. In: ICLR (2020)

    Google Scholar 

  3. Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: a holistic approach to semi-supervised learning. Adv. Neural Inf. Proc. Syst. 32 (2019)

    Google Scholar 

  4. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. ArXiv arXiv:2004.10934 (2020)

  5. Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)

    Google Scholar 

  6. Caine, B., et al.: Pseudo-labeling for scalable 3D object detection. ArXiv arXiv:2103.02093 (2021)

  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. Chong, Z., et al.: Monodistill: learning spatial features for monocular 3D object detection. ArXiv arXiv:2201.10830 (2022)

  9. Choy, C., Gwak, J., Savarese, S.: 4D spatio-temporal convnets: minkowski convolutional neural networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3070–3079 (2019)

    Google Scholar 

  10. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: richly-annotated 3D reconstructions of indoor scenes. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2432–2443 (2017)

    Google Scholar 

  11. Feng, D., Zhou, Y., Xu, C., Tomizuka, M., Zhan, W.: A simple and efficient multi-task network for 3D object detection and road understanding. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7067–7074. IEEE (2021)

    Google Scholar 

  12. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361 (2012)

    Google Scholar 

  13. Graham, B., Engelcke, M., Maaten, L.V.D.: 3D semantic segmentation with submanifold sparse convolutional networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9224–9232 (2018)

    Google Scholar 

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

    Google Scholar 

  15. Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2(7) (2015)

  16. Huang, T., Liu, Z., Chen, X., Bai, X.: EPNet: enhancing point features with image semantics for 3D object detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12360, pp. 35–52. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58555-6_3

    Chapter  Google Scholar 

  17. Janoch, A., et al.: A category-level 3-D object dataset: Putting the Kinect to work. In: ICCV Workshops (2011)

    Google Scholar 

  18. Jaritz, M., Vu, T.H., de Charette, R., Wirbel, É., Pérez, P.: xMUDA: cross-modal unsupervised domain adaptation for 3D semantic segmentation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12602–12611 (2020)

    Google Scholar 

  19. Jeong, J., Lee, S., Kim, J., Kwak, N.: Consistency-based semi-supervised learning for object detection. In: NeurIPS (2019)

    Google Scholar 

  20. Jiang, B., Luo, R., Mao, J., Xiao, T., Jiang, Y.: Acquisition of localization confidence for accurate object detection. In: Proceedings of the European conference on computer vision (ECCV), pp. 784–799 (2018)

    Google Scholar 

  21. Kim, T., Oh, J., Kim, N., Cho, S., Yun, S.Y.: Comparing Kullback-Leibler divergence and mean squared error loss in knowledge distillation. In: IJCAI (2021)

    Google Scholar 

  22. Kuhn, H.W.: The hungarian method for the assignment problem. Naval Res. Logist. Quart. 2, 83–97 (1955)

    Article  MathSciNet  MATH  Google Scholar 

  23. Lahoud, J., Ghanem, B.: 2D-driven 3D object detection in RGB-D images. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 4632–4640 (2017)

    Google Scholar 

  24. Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: ICLR (2017)

    Google Scholar 

  25. Lee, D.H., et al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol. 3, p. 896 (2013)

    Google Scholar 

  26. Li, H., Wu, Z., Shrivastava, A., Davis, L.S.: Rethinking pseudo labels for semi-supervised object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 1314–1322 (2022)

    Google Scholar 

  27. Li, Y.J., Park, J., O’Toole, M., Kitani, K.: Modality-agnostic learning for radar-lidar fusion in vehicle detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 918–927 (2022)

    Google Scholar 

  28. Liang, Z., Zhang, M., Zhang, Z., Zhao, X., Pu, S.: Rangercnn: towards fast and accurate 3D object detection with range image representation. ArXiv arXiv:2009.00206 (2020)

  29. 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 

  30. Lin, T.Y., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42, 318–327 (2020)

    Article  Google Scholar 

  31. Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. Lecture Notes in Computer Science, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  32. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  33. Liu, Y.C., et al.: Unbiased teacher for semi-supervised object detection. In: ICLR (2021)

    Google Scholar 

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

  35. Liu, Y., Yi, L., Zhang, S., Fan, Q., Funkhouser, T.A., Dong, H.: P4contrast: contrastive learning with pairs of point-pixel pairs for RGB-D scene understanding. ArXiv arXiv:2012.13089 (2020)

  36. Liu, Z., Qi, X., Fu, C.W.: 3D-to-2D distillation for indoor scene parsing. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4462–4472 (2021)

    Google Scholar 

  37. Park, J.D., Weng, X., Man, Y., Kitani, K.: Multi-modality task cascade for 3D object detection. In: BMVC (2021)

    Google Scholar 

  38. Qi, C., Chen, X., Litany, O., Guibas, L.: Imvotenet: boosting 3D object detection in point clouds with image votes. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4403–4412 (2020)

    Google Scholar 

  39. Qi, C., Litany, O., He, K., Guibas, L.: Deep hough voting for 3D object detection in point clouds. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9276–9285 (2019)

    Google Scholar 

  40. Qi, C., Liu, W., Wu, C., Su, H., Guibas, L.: Frustum pointnets for 3D object detection from RGB-D data. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 918–927 (2018)

    Google Scholar 

  41. Qi, C., Yi, L., Su, H., Guibas, L.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: NIPS (2017)

    Google Scholar 

  42. Qi, C., et al.: Offboard 3D object detection from point cloud sequences. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6130–6140 (2021)

    Google Scholar 

  43. Rasmus, A., Berglund, M., Honkala, M., Valpola, H., Raiko, T.: Semi-supervised learning with ladder networks. Adv. Neural Inf. Process. Syst. 28 (2015)

    Google Scholar 

  44. Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

    Google Scholar 

  45. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2015)

    Article  Google Scholar 

  46. Rezatofighi, S.H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I.D., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 658–666 (2019)

    Google Scholar 

  47. Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Adv. Neural Inf. Process. Syst. 29 (2016)

    Google Scholar 

  48. Shi, S., et al.: PV-RCNN: point-voxel feature set abstraction for 3d object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10526–10535 (2020)

    Google Scholar 

  49. Shi, S., Wang, X., Li, H.: Pointrcnn: 3D object proposal generation and detection from point cloud. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–779 (2019)

    Google Scholar 

  50. Shi, S., Wang, Z., Shi, J., Wang, X., Li, H.: From points to parts: 3D object detection from point cloud with part-aware and part-aggregation network. IEEE Trans. Pattern Anal. Mach. Intell. (2020)

    Google Scholar 

  51. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54

    Chapter  Google Scholar 

  52. Sindagi, V., Zhou, Y., Tuzel, O.: Mvx-net: multimodal voxelnet for 3D object detection. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 7276–7282 (2019)

    Google Scholar 

  53. Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596–608 (2020)

    Google Scholar 

  54. Sohn, K., Zhang, Z., Li, C.L., Zhang, H., Lee, C.Y., Pfister, T.: A simple semi-supervised learning framework for object detection. ArXiv arXiv:2005.04757 (2020)

  55. Song, G., Liu, Y., Wang, X.: Revisiting the sibling head in object detector. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11560–11569 (2020)

    Google Scholar 

  56. Song, S., Lichtenberg, S.P., Xiao, J.: Sun RGB-D: a RGB-D scene understanding benchmark suite. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 567–576 (2015)

    Google Scholar 

  57. Sun, P., et al.: Scalability in perception for autonomous driving: waymo open dataset. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2443–2451 (2020)

    Google Scholar 

  58. Sun, P., et al.: Sparse R-CNN: end-to-end object detection with learnable proposals. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14449–14458 (2021)

    Google Scholar 

  59. Tang, Y., Chen, W., Luo, Y., Zhang, Y.: Humble teachers teach better students for semi-supervised object detection. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3131–3140 (2021)

    Google Scholar 

  60. Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Process. Syst. 30 (2017)

    Google Scholar 

  61. Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9626–9635 (2019)

    Google Scholar 

  62. Vora, S., Lang, A.H., Helou, B., Beijbom, O.: Pointpainting: sequential fusion for 3d object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4603–4611 (2020)

    Google Scholar 

  63. Wang, C.H., Chen, H.W., Fu, L.C.: Vpfnet: voxel-pixel fusion network for multi-class 3D object detection. ArXiv arXiv:2111.00966 (2021)

  64. Wang, H., Cong, Y., Litany, O., Gao, Y., Guibas, L.J.: 3dioumatch: leveraging IOU prediction for semi-supervised 3D object detection. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14610–14619 (2021)

    Google Scholar 

  65. Wang, J., Gang, H., Ancha, S., Chen, Y.T., Held, D.: Semi-supervised 3D object detection via temporal graph neural networks. In: 2021 International Conference on 3D Vision (3DV), pp. 413–422 (2021)

    Google Scholar 

  66. Wang, Z., Jia, K.: Frustum convnet: sliding frustums to aggregate local point-wise features for amodal. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1742–1749 (2019)

    Google Scholar 

  67. Xiao, J., Owens, A., Torralba, A.: Sun3d: a database of big spaces reconstructed using sfm and object labels. In: 2013 IEEE International Conference on Computer Vision, pp. 1625–1632 (2013)

    Google Scholar 

  68. Xie, L., Xiang, C., Yu, Z., Xu, G., Yang, Z., Cai, D., He, X.: Pi-RCNN: an efficient multi-sensor 3D object detector with point-based attentive cont-conv fusion module. AAAI arXiv:1911.06084 (2020)

  69. Xu, C., et al.: Image2point: 3D point-cloud understanding with pretrained 2D convnets. arXiv preprint arXiv:2106.04180 (2021)

  70. Xu, C., et al.: You only group once: efficient point-cloud processing with token representation and relation inference module. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4589–4596. IEEE (2021)

    Google Scholar 

  71. Xu, M., et al.: End-to-end semi-supervised object detection with soft teacher. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3040–3049 (2021)

    Google Scholar 

  72. Yan, Y., Mao, Y., Li, B.: Second: sparsely embedded convolutional detection. Sensors (Basel, Switzerland) 18 (2018)

    Google Scholar 

  73. Yang, Q., Wei, X., Wang, B., Hua, X., Zhang, L.: Interactive self-training with mean teachers for semi-supervised object detection. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5937–5946 (2021)

    Google Scholar 

  74. Yang, Z., Sun, Y., Liu, S., Jia, J.: 3DSSD: point-based 3D single stage object detector. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11037–11045 (2020)

    Google Scholar 

  75. Yin, T., Zhou, X., Krähenbühl, P.: Multimodal virtual point 3D detection. In: NeurIPS (2021)

    Google Scholar 

  76. Yoo, J.H., Kim, Y., Kim, J., Choi, J.W.: 3D-CVF: generating joint camera and LiDAR features using cross-view spatial feature fusion for 3D object detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12372, pp. 720–736. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58583-9_43

    Chapter  Google Scholar 

  77. Zhang, B., et al.: Flexmatch: boosting semi-supervised learning with curriculum pseudo labeling. Adv. Neural. Inf. Process. Syst. 34, 18408–18419 (2021)

    Google Scholar 

  78. Zhang, H., Cissé, M., Dauphin, Y., Lopez-Paz, D.: mixup: beyond empirical risk minimization. In: ICLR (2018)

    Google Scholar 

  79. Zhao, L., Zhou, H., Zhu, X., Song, X., Li, H., Tao, W.: LIF-SEG: lidar and camera image fusion for 3d lidar semantic segmentation. ArXiv arXiv:2108.07511 (2021)

  80. Zhao, N., Chua, T.S., Lee, G.H.: SESS: self-ensembling semi-supervised 3D object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11076–11084 (2020)

    Google Scholar 

  81. feng Zhou, Q., Yu, C., Wang, Z., Qian, Q., Li, H.: Instant-teaching: an end-to-end semi-supervised object detection framework. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4079–4088 (2021)

    Google Scholar 

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

    Google Scholar 

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Acknowledgements

Co-authors from UC Berkeley were sponsored by Berkeley Deep Drive (BDD).

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Jinhyung Park

  2. University of California, Berkeley, CA, 94720, USA

    Chenfeng Xu, Yiyang Zhou, Masayoshi Tomizuka & Wei Zhan

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  1. Jinhyung Park
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  4. Masayoshi Tomizuka
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  5. Wei Zhan
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Correspondence to Chenfeng Xu .

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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

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Park, J., Xu, C., Zhou, Y., Tomizuka, M., Zhan, W. (2022). DetMatch: Two Teachers are Better than One for Joint 2D and 3D Semi-Supervised Object Detection. 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 13670. Springer, Cham. https://doi.org/10.1007/978-3-031-20080-9_22

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