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We present RangeRCNN, a novel and effective 3D object detection framework based on the range image representation.
Z. Yang, Y. Sun, S. Liu, X. Shen, and J. Jia, “Std: Sparse-to-dense 3d object detector for point cloud,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1951–1960
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B. Li, T. Zhang, and T. Xia, “Vehicle detection from 3d lidar using fully convolutional network,” in Proceedings of Robotics: Science and Systems (RSS) , 2016
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 4, pp. 834–848, 2017
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X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 6526–6534
2017
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C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in Neural Information Processing Systems , 2017, pp. 5099–5108
2017
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C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
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Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors , vol. 18, no. 10, p. 3337, 2018
2018
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B. Graham, M. Engelcke, and L. van der Maaten, “3d semantic segmentation with submanifold sparse convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 9224–9232
2018
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J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, “Joint 3d proposal generation and object detection from view aggregation,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 1–8
2018
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M. Liang, B. Yang, S. Wang, and R. Urtasun, “Deep continuous fusion for multi-sensor 3d object detection,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 641–656
2018
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Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 4490–4499
2018
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C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum pointnets for 3d object detection from rgb-d data,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 918–927
2018
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Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “Pointcnn: Convolution on x-transformed points,” in Advances in neural information processing systems , 2018, pp. 820–830
2018
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B. Wu, A. Wan, X. Yue, and K. Keutzer, “Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1887–1893
2018
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A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, “Rangenet++: Fast and accurate lidar semantic segmentation,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 4213–4220
2019
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D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai, and R. Yang, “Iou loss for 2d/3d object detection,” in 2019 International Conference on 3D Vision (3DV) , 2019, pp. 85–94
2019
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Y. Chen, S. Liu, X. Shen, and J. Jia, “Fast point r-cnn,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2019
2019
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S. Shi, Z. Wang, J. Shi, X. Wang, and H. Li, “From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Closest in time.
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G. P. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez, and C. K. Wellington, “Lasernet: An efficient probabilistic 3d object detector for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019, pp. 12 677–12 686
2019
Cited alongside, same era.
M. Liang, B. Yang, Y. Chen, R. Hu, and R. Urtasun, “Multi-task multi-sensor fusion for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019, pp. 7345–7353
2019
Cited alongside, same era.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019, pp. 12 697–12 705
2019
Cited alongside, same era.
Z. Wang and K. Jia, “Frustum convnet: Sliding frustums to aggregate local point-wise features for amodal 3d object detection,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019, pp. 1742–1749
2019
Cited alongside, same era.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object progposal generation and detection from point cloud,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 770–779
2019
Cited alongside, same era.
C. R. Qi, O. Litany, K. He, and L. Guibas, “Deep hough voting for 3d object detection in point clouds,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 9276–9285
2019
Cited alongside, same era.
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “Semantickitti: A dataset for semantic scene understanding of lidar sequences,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 9297–9307
2019
Cited alongside, same era.
C. He, H. Zeng, J. Huang, X.-S. Hua, and L. Zhang, “Structure aware single-stage 3d object detection from point cloud,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 873–11 882
2020
Closest in time.
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. Li, “Pv-rcnn: Point-voxel feature set abstraction for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 529–10 538
2020
Closest in time.
W. Shi and R. Rajkumar, “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 , 2020, pp. 1711–1719
2020
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Z. Yang, Y. Sun, S. Liu, and J. Jia, “3dssd: Point-based 3d single stage object detector,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 040–11 048
2020
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2020
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T. Huang, Z. Liu, X. Chen, and X. Bai, “Epnet: Enhancing point features with image semantics for 3d object detection,” Proceedings of the European Conference on Computer Vision (ECCV) , 2020
2020
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2020
Closest in time.