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Modern neural networks use building blocks such as convolutions that are equivariant to arbitrary 2D translations.
Zwicke, P., Kiss, I.: A new implementation of the mellin transform and its application to radar classification of ships. TPAMI (1983)
1983
Earlier work this paper cites.
Freeman, W., Adelson, E.: The design and use of steerable filters. TPAMI (1991)
1991
Earlier work this paper cites.
Burns, B., Weiss, R., Riseman, E.: The non-existence of general-case view-invariants. In: Geometric invariance in computer vision (1992)
1992
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE (1998)
1998
Earlier work this paper cites.
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge university press (2003)
2003
Earlier work this paper cites.
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. TIP (2004)
2004
Earlier work this paper cites.
Saxena, A., Driemeyer, J., Ng, A.: Robotic grasping of novel objects using vision. IJRR (2008)
2008
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: CVPR (2009)
2009
Earlier work this paper cites.
Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks (2009)
2009
Earlier work this paper cites.
Payet, N., Todorovic, S.: From contours to 3 3 D object detection and pose estimation. In: ICCV (2011)
2011
Earlier work this paper cites.
Fidler, S., Dickinson, S., Urtasun, R.: 3 3 D object detection and viewpoint estimation with a deformable 3 3 D cuboid model. In: NeurIPS (2012)
2012
Earlier work this paper cites.
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: CVPR (2012)
2012
Earlier work this paper cites.
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The KITTI dataset. IJRR (2013)
2013
Earlier work this paper cites.
Kumar, A., Prabhakaran, V.: Estimation of bandlimited signals from the signs of noisy samples. In: ICASSP (2013)
2013
Earlier work this paper cites.
Cohen, T., Welling, M.: Learning the irreducible representations of commutative lie groups. In: ICML (2014)
2014
Earlier work this paper cites.
Kanazawa, A., Sharma, A., Jacobs, D.: Locally scale-invariant convolutional neural networks. In: NeurIPS Workshops (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Chen, X., Kundu, K., Zhu, Y., Berneshawi, A., Ma, H., Fidler, S., Urtasun, R.: 3 3 D object proposals for accurate object class detection. In: NeurIPS (2015)
2015
Earlier work this paper cites.
Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)
2015
Earlier work this paper cites.
Pepik, B., Stark, M., Gehler, P., Schiele, B.: Multi-view and 3 3 D deformable part models. TPAMI (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: NeurIPS (2015)
2015
Earlier work this paper cites.
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2015)
2015
Earlier work this paper cites.
Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., Urtasun, R.: Monocular 3 3 D object detection for autonomous driving. In: CVPR (2016)
2016
Earlier work this paper cites.
Cohen, T., Welling, M.: Group equivariant convolutional networks. In: ICML (2016)
2016
Earlier work this paper cites.
Dieleman, S., De Fauw, J., Kavukcuoglu, K.: Exploiting cyclic symmetry in convolutional neural networks. In: ICML (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 2 2 D and 3 3 D vehicle analysis from monocular image. In: CVPR (2017)
2017
Earlier work this paper cites.
Ganea, O.E., Bécigneul, G., Hofmann, T.: Hyperbolic neural networks. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Henriques, J., Vedaldi, A.: Warped convolutions: Efficient invariance to spatial transformations. In: ICML (2017)
2017
Earlier work this paper cites.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR (2017)
2017
Earlier work this paper cites.
Marcos, D., Volpi, M., Komodakis, N., Tuia, D.: Rotation equivariant vector field networks. In: ICCV (2017)
2017
Earlier work this paper cites.
Worrall, D., Garbin, S., Turmukhambetov, D., Brostow, G.: Harmonic networks: Deep translation and rotation equivariance. In: CVPR (2017)
2017
Earlier work this paper cites.
Alhaija, H., Mustikovela, S., Mescheder, L., Geiger, A., Rother, C.: Augmented reality meets computer vision: Efficient data generation for urban driving scenes. IJCV (2018)
2018
Earlier work this paper cites.
Cohen, T., Geiger, M., Köhler, J., Welling, M.: Spherical CNNs. In: ICLR (2018)
2018
Earlier work this paper cites.
Esteves, C., Allen-Blanchette, C., Zhou, X., Daniilidis, K.: Polar transformer networks. In: ICLR (2018)
2018
Earlier work this paper cites.
Marcos, D., Kellenberger, B., Lobry, S., Tuia, D.: Scale equivariance in CNNs with vector fields. In: ICML Workshops (2018)
2018
Earlier work this paper cites.
Rematas, K., Kemelmacher-Shlizerman, I., Curless, B., Seitz, S.: Soccer on your tabletop. In: CVPR (2018)
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
Weiler, M., Hamprecht, F., Storath, M.: Learning steerable filters for rotation equivariant CNNs. In: CVPR (2018)
2018
Cited alongside, same era.
Wilk, M.v.d., Bauer, M., John, S., Hensman, J.: Learning invariances using the marginal likelihood. In: NeurIPS (2018)
2018
Cited alongside, same era.
Worrall, D., Brostow, G.: Cubenet: Equivariance to 3 3 D rotation and translation. In: ECCV (2018)
2018
Cited alongside, same era.
Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. In: CVPR (2018)
2018
Cited alongside, same era.
Wang, Y., Chen, X., You, Y., Li, L., Hariharan, B., Campbell, M., Weinberger, K., Chao, W.L.: Train in Germany, test in the USA: Making 3 3 D object detectors generalize. In: CVPR (2020)
2020
Later among the works it cites.
Yang, G., Ramanan, D.: Upgrading optical flow to 3 3 D scene flow through optical expansion. In: CVPR (2020)
2020
Later among the works it cites.
Bronstein, M.: Convolution from first principles. https://towardsdatascience.com/deriving-convolution-from-first-principles-4ff124888028 , accessed: 2021-08-13
2021
Later among the works it cites.
2021
Later among the works it cites.
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Brazil, G., Liu, X.: M 3 3 D-RPN: Monocular 3 3 D region proposal network for object detection. In: ICCV (2019)
2019
Cited alongside, same era.
Ghosh, R., Gupta, A.: Scale steerable filters for locally scale-invariant convolutional neural networks. In: ICML Workshops (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Liu, L., Lu, J., Xu, C., Tian, Q., Zhou, J.: Deep fitting degree scoring network for monocular 3 3 D object detection. In: CVPR (2019)
2019
Cited alongside, same era.
Ma, X., Wang, Z., Li, H., Zhang, P., Ouyang, W., Fan, X.: Accurate monocular 3 3 D object detection via color-embedded 3 3 D reconstruction for autonomous driving. In: ICCV (2019)
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: PyTorch: An imperative style, high-performance deep learning library. In: NeurIPS (2019)
2019
Cited alongside, same era.
Shi, S., Wang, X., Li, H.: PointRCNN: 3 3 D object proposal generation and detection from point cloud. In: CVPR (2019)
2019
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
Jansson, Y., Lindeberg, T.: Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales. IJCV (2021)
2021
Later among the works it cites.
Kumar, A., Brazil, G., Liu, X.: GrooMeD-NMS: Grouped mathematically differentiable NMS for monocular 3 3 D object detection. In: CVPR (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Liu, Y., Yixuan, Y., Liu, M.: Ground-aware monocular 3 3 D object detection for autonomous driving. Robotics and Automation Letters (2021)
2021
Later among the works it cites.
Liu, Z., Zhou, D., Lu, F., Fang, J., Zhang, L.: AutoShape: Real-time shape-aware monocular 3 3 D object detection. In: ICCV (2021)
2021
Later among the works it cites.
Lu, Y., Ma, X., Yang, L., Zhang, T., Liu, Y., Chu, Q., Yan, J., Ouyang, W.: Geometry uncertainty projection network for monocular 3 3 D object detection. In: ICCV (2021)
2021
Later among the works it cites.
Ma, X., Zhang, Y., Xu, D., Zhou, D., Yi, S., Li, H., Ouyang, W.: Delving into localization errors for monocular 3 3 D object detection. In: CVPR (2021)
2021
Later among the works it cites.
Park, D., Ambrus, R., Guizilini, V., Li, J., Gaidon, A.: Is Pseudo-LiDAR needed for monocular 3 3 D object detection? In: ICCV (2021)
2021
Later among the works it cites.
Reading, C., Harakeh, A., Chae, J., Waslander, S.: Categorical depth distribution network for monocular 3 3 D object detection. In: CVPR (2021)
2021
Later among the works it cites.
Shi, X., Ye, Q., Chen, X., Chen, C., Chen, Z., Kim, T.K.: Geometry-based distance decomposition for monocular 3 3 D object detection. In: ICCV (2021)
2021
Later among the works it cites.
Simonelli, A., Bulò, S., Porzi, L., Kontschieder, P., Ricci, E.: Are we missing confidence in Pseudo-LiDAR methods for monocular 3 3 D object detection? In: ICCV (2021)
2021
Later among the works it cites.
Sosnovik, I., Moskalev, A., Smeulders, A.: DISCO: accurate discrete scale convolutions. In: BMVC (2021)
2021
Later among the works it cites.
Sosnovik, I., Moskalev, A., Smeulders, A.: Scale equivariance improves siamese tracking. In: WACV (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Wang, L., Du, L., Ye, X., Fu, Y., Guo, G., Xue, X., Feng, J., Zhang, L.: Depth-conditioned dynamic message propagation for monocular 3 3 D object detection. In: CVPR (2021)
2021
Later among the works it cites.
Wang, L., Zhang, L., Zhu, Y., Zhang, Z., He, T., Li, M., Xue, X.: Progressive coordinate transforms for monocular 3 3 D object detection. In: NeurIPS (2021)
2021
Later among the works it cites.
Wang, R., Walters, R., Yu, R.: Incorporating symmetry into deep dynamics models for improved generalization. In: ICLR (2021)
2021
Later among the works it cites.
Wang, Y., Guizilini, V., Zhang, T., Wang, Y., Zhao, H., Solomon, J.: DETR3D: 3 3 D object detection from multi-view images via 3 3 D-to- 2 2 D queries. In: CoRL (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Wu, Y., Johnson, J.: Rethinking “batch” in batchnorm. arXiv preprint arXiv:2105.07576 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Zhang, Y., Lu, J., Zhou, J.: Objects are different: Flexible monocular 3 3 D object detection. In: CVPR (2021)
2021
Later among the works it cites.
Zhou, A., Knowles, T., Finn, C.: Meta-learning symmetries by reparameterization. In: ICLR (2021)
2021
Later among the works it cites.
Zhou, Y., He, Y., Zhu, H., Wang, C., Li, H., Jiang, Q.: MonoEF: Extrinsic parameter free monocular 3 3 D object detection. TPAMI (2021)
2021
Later among the works it cites.
Zou, Z., Ye, X., Du, L., Cheng, X., Tan, X., Zhang, L., Feng, J., Xue, X., Ding, E.: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3 3 D object detection. In: ICCV (2021)
2021
Later among the works it cites.
The KITTI Vision Benchmark Suite. http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d , accessed: 2022-07-03
2022
Closest in time.
Chong, Z., Ma, X., Zhang, H., Yue, Y., Li, H., Wang, Z., Ouyang, W.: MonoDistill: Learning spatial features for monocular 3 3 D object detection. In: ICLR (2022)
2022
Closest in time.
Liu, X., Xue, N., Wu, T.: Learning auxiliary monocular contexts helps monocular 3 3 D object detection. In: AAAI (2022)
2022
Closest in time.