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Lidar Point Cloud Guided Monocular 3D Object Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13661))

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Abstract

Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the detector can achieve very close accuracy compared to the one using the ground-truth labels. We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels. Motivated by the conclusions above and considering the precise LiDAR 3D measurement, we propose a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in 3D space, such pseudo labels can replace manually annotated labels in the training of monocular 3D detectors, since their 3D location information is precise. LPCG can be applied into any monocular 3D detector to fully use massive unlabeled data in a self-driving system. As a result, in KITTI benchmark, we take the first place on both monocular 3D and BEV (bird’s-eye-view) detection with a significant margin. In Waymo benchmark, our method using 10% labeled data achieves comparable accuracy to the baseline detector using 100% labeled data. The codes are released at https://github.com/SPengLiang/LPCG.

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Notes

  1. 1.

    From RTM3D official implementation.

References

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

    Google Scholar 

  2. Chen, H., Huang, Y., Tian, W., Gao, Z., Xiong, L.: MonoRUn: monocular 3D object detection by reconstruction and uncertainty propagation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10379–10388 (2021)

    Google Scholar 

  3. Chen, X., Kundu, K., Zhu, Y., Ma, H., Fidler, S., Urtasun, R.: 3D object proposals using stereo imagery for accurate object class detection. IEEE Trans. Pattern Anal. Mach. Intell. 40(5), 1259–1272 (2017)

    Article  Google Scholar 

  4. Chen, Y., Tai, L., Sun, K., Li, M.: MonoPair: Monocular 3D object detection using pairwise spatial relationships. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12093–12102 (2020)

    Google Scholar 

  5. Chu, X., et al.: Neighbor-vote: improving monocular 3d object detection through neighbor distance voting. arXiv preprint arXiv:2107.02493 (2021)

  6. Ding, M., et al.: Learning depth-guided convolutions for monocular 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11672–11681 (2020)

    Google Scholar 

  7. Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol. 96, pp. 226–231 (1996)

    Google Scholar 

  8. 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. IEEE (2012)

    Google Scholar 

  9. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  10. 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 

  11. Kumar, A., Brazil, G., Liu, X.: GrooMeD-NMS: grouped mathematically differentiable NMS for monocular 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8973–8983 (2021)

    Google Scholar 

  12. 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 Conference on Computer Vision and Pattern Recognition, pp. 12697–12705 (2019)

    Google Scholar 

  13. Li, P., Zhao, H., Liu, P., Cao, F.: RTM3D: real-time monocular 3D detection from object keypoints for autonomous driving. arXiv preprint arXiv:2001.03343 (2020)

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

    Chapter  Google Scholar 

  15. Liu, Y., Yixuan, Y., Liu, M.: Ground-aware monocular 3D object detection for autonomous driving. IEEE Robot. Autom. Lett. 6(2), 919–926 (2021)

    Article  Google Scholar 

  16. Liu, Z., Zhou, D., Lu, F., Fang, J., Zhang, L.: Autoshape: real-time shape-aware monocular 3D object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15641–15650 (2021)

    Google Scholar 

  17. Lu, Y., et al.: Geometry uncertainty projection network for monocular 3D object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3111–3121 (2021)

    Google Scholar 

  18. Ma, X., Liu, S., Xia, Z., Zhang, H., Zeng, X., Ouyang, W.: Rethinking pseudo-LiDAR representation. arXiv preprint arXiv:2008.04582 (2020)

  19. Ma, X., Wang, Z., Li, H., Zhang, P., Ouyang, W., Fan, X.: Accurate monocular 3D object detection via color-embedded 3D reconstruction for autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6851–6860 (2019)

    Google Scholar 

  20. Ma, X., et al.: Delving into localization errors for monocular 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4721–4730 (2021)

    Google Scholar 

  21. Manhardt, F., Kehl, W., Gaidon, A.: ROI-10D: monocular lifting of 2D detection to 6D pose and metric shape. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2069–2078 (2019)

    Google Scholar 

  22. 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 

  23. Peng, L., Liu, F., Yan, S., He, X., Cai, D.: OCM3D: object-centric monocular 3D object detection. arXiv preprint arXiv:2104.06041 (2021)

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

    Google Scholar 

  25. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)

    Google Scholar 

  26. 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)

  27. Qin, Z., Wang, J., Lu, Y.: MonoGRNet: a geometric reasoning network for monocular 3D object localization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8851–8858 (2019)

    Google Scholar 

  28. 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 

  29. Shi, S., et al.: PV-RCNN: point-voxel feature set abstraction for 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10529–10538 (2020)

    Google Scholar 

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

    Google Scholar 

  31. 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. arXiv preprint arXiv:1907.03670 (2019)

  32. Shi, W., Rajkumar, R.: Point-GNN: graph neural network for 3D object detection in a point cloud. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1711–1719 (2020)

    Google Scholar 

  33. Shi, X., Ye, Q., Chen, X., Chen, C., Chen, Z., Kim, T.K.: Geometry-based distance decomposition for monocular 3D object detection. arXiv preprint arXiv:2104.03775 (2021)

  34. Simonelli, A., Bulo, S.R., Porzi, L., Kontschieder, P., Ricci, E.: Are we missing confidence in pseudo-LiDAR methods for monocular 3D object detection? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3225–3233 (2021)

    Google Scholar 

  35. Simonelli, A., Bulo, S.R., Porzi, L., López-Antequera, M., Kontschieder, P.: Disentangling monocular 3D object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1991–1999 (2019)

    Google Scholar 

  36. 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 

  37. Toussaint, G.T.: Solving geometric problems with the rotating calipers. In: Proceedings of IEEE Melecon, vol. 83, p. A10 (1983)

    Google Scholar 

  38. Wang, L., et al.: Depth-conditioned dynamic message propagation for monocular 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 454–463 (2021)

    Google Scholar 

  39. Wang, L., Zhang, L., Zhu, Y., Zhang, Z., He, T., Li, M., Xue, X.: Progressive coordinate transforms for monocular 3D object detection. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  40. 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 Conference on Computer Vision and Pattern Recognition, pp. 8445–8453 (2019)

    Google Scholar 

  41. Weng, X., Kitani, K.: Monocular 3D object detection with pseudo-LiDAR point cloud. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)

    Google Scholar 

  42. Yang, Z., Sun, Y., Liu, S., Jia, J.: 3DSSD: point-based 3D single stage object detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11040–11048 (2020)

    Google Scholar 

  43. Ye, M., Xu, S., Cao, T.: HVNet: hybrid voxel network for lidar based 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1631–1640 (2020)

    Google Scholar 

  44. Zakharov, S., Kehl, W., Bhargava, A., Gaidon, A.: Autolabeling 3D objects with differentiable rendering of SDF shape priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12224–12233 (2020)

    Google Scholar 

  45. Zhang, Y., Lu, J., Zhou, J.: Objects are different: flexible monocular 3D object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3289–3298 (2021)

    Google Scholar 

  46. Zheng, W., Tang, W., Jiang, L., Fu, C.W.: SE-SSD: self-ensembling single-stage object detector from point cloud. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14494–14503 (2021)

    Google Scholar 

  47. 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 

  48. Zhou, Y., He, Y., Zhu, H., Wang, C., Li, H., Jiang, Q.: Monocular 3D object detection: an extrinsic parameter free approach. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7556–7566 (2021)

    Google Scholar 

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Acknowledgments

This work was supported in part by The National Key Research and Development Program of China (Grant Nos: 2018AAA0101400), in part by The National Nature Science Foundation of China (Grant Nos: 62036009, U1909203, 61936006, 61973271), in part by Innovation Capability Support Program of Shaanxi (Program No. 2021TD-05).

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Authors and Affiliations

  1. State Key Lab of CAD &CG, Zhejiang University, Hangzhou, China

    Liang Peng, Zhengxu Yu, Senbo Yan, Haifeng Liu & Deng Cai

  2. Fabu Inc., Hangzhou, China

    Liang Peng, Senbo Yan, Dan Deng, Zheng Yang & Deng Cai

  3. State Key Lab of Industrial Control and Technology, Zhejiang University, Hangzhou, China

    Fei Liu

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  1. Liang Peng
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Correspondence to Deng Cai .

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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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Peng, L. et al. (2022). Lidar Point Cloud Guided Monocular 3D 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 13661. Springer, Cham. https://doi.org/10.1007/978-3-031-19769-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-19769-7_8

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