Rethinking Pseudo-LiDAR Representation

Jörgensen, E., Zach, C., Kahl, F.: Monocular 3d object detection and box fitting trained end-to-end using intersection-over-union loss. CoRR abs/1906.08070

Original

1906

Earlier work this paper cites.

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)

2012

Earlier work this paper cites.

Chen, X., Kundu, K., Zhu, Y., Berneshawi, A.G., Ma, H., Fidler, S., Urtasun, R.: 3d object proposals for accurate object class detection. In: Advances in Neural Information Processing Systems. pp. 424–432 (2015)

2015

Earlier work this paper cites.

Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 1440–1448 (2015)

2015

Earlier work this paper cites.

Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp. 91–99 (2015)

2015

Earlier work this paper cites.

Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., Urtasun, R.: Monocular 3d object detection for autonomous driving. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2147–2156 (2016)

2016

Earlier work this paper cites.

Dai, J., Li, Y., He, K., Sun, J.: R-fcn: Object detection via region-based fully convolutional networks. In: Advances in neural information processing systems. pp. 379–387 (2016)

2016

Earlier work this paper cites.

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

2016

Earlier work this paper cites.

Chabot, F., Chaouch, M., Rabarisoa, J., Teuliere, C., Chateau, T.: Deep manta: A coarse-to-fine many-task network for joint 2d and 3d vehicle analysis from monocular image. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2040–2049 (2017)

2017

Earlier work this paper cites.

Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3d object detection network for autonomous driving. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

2017

Earlier work this paper cites.

Godard, C., Mac Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 270–279 (2017)

2017

Earlier work this paper cites.

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)

2017

Earlier work this paper cites.

Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

2017

Earlier work this paper cites.

Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)

2017

Earlier work this paper cites.

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)

2017

Cited alongside, same era.

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)

2017

Cited alongside, same era.

Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: Advances in neural information processing systems. pp. 5099–5108 (2017)

2017

Cited alongside, same era.

Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1492–1500 (2017)

2017

Cited alongside, same era.

Li, B., Ouyang, W., Sheng, L., Zeng, X., Wang, X.: Gs3d: An efficient 3d object detection framework for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1019–1028 (2019)

2019

Later among the works it cites.

Li, P., Chen, X., Shen, S.: Stereo r-cnn based 3d object detection for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

2019

Later among the works it cites.

Liu, L., Lu, J., Xu, C., Tian, Q., Zhou, J.: Deep fitting degree scoring network for monocular 3d object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1057–1066 (2019)

2019

Later among the works it cites.

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: The IEEE International Conference on Computer Vision (ICCV) (October 2019)

2019