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Recent advances in 3D semantic segmentation with deep neural networks have shown remarkable success, with rapid performance increase on available datasets.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International journal of computer vision 88
2010
Earlier work this paper cites.
Nathan Silberman, Derek Hoiem, P.K., Fergus, R.: Indoor segmentation and support inference from rgbd images. In: ECCV (2012)
2012
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proc. Computer Vision and Pattern Recognition (CVPR), IEEE (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.
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
Earlier work this paper cites.
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Riegler, G., Osman Ulusoy, A., Geiger, A.: Octnet: Learning deep 3d representations at high resolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3577–3586 (2017)
2017
Earlier work this paper cites.
Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks 106
2018
Earlier work this paper cites.
Dai, A., Nießner, M.: 3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 452–468 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Graham, B., Engelcke, M., van der Maaten, L.: 3d semantic segmentation with submanifold sparse convolutional networks. CVPR (2018)
2018
Earlier work this paper cites.
Van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv e-prints pp. arXiv–1807 (2018)
2018
Earlier work this paper cites.
Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Y.: Spidercnn: Deep learning on point sets with parameterized convolutional filters. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 87–102 (2018)
2018
Earlier work this paper cites.
Yan, Y., Mao, Y., Li, B.: Second: Sparsely embedded convolutional detection. Sensors (Basel, Switzerland) 18
2018
Earlier work this paper cites.
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In: Proc. of the IEEE/CVF International Conf. on Computer Vision (ICCV) (2019)
2019
Earlier work this paper cites.
Biasutti, P., Lepetit, V., Aujol, J.F., Brédif, M., Bugeau, A.: Lu-net: An efficient network for 3d lidar point cloud semantic segmentation based on end-to-end-learned 3d features and u-net. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops. pp. 0–0 (2019)
2019
Earlier work this paper cites.
Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3075–3084 (2019)
2019
Cited alongside, same era.
Gupta, A., Dollar, P., Girshick, R.: LVIS: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
2019
Cited alongside, same era.
Manhardt, F., Kehl, W., Gaidon, A.: Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2069–2078 (2019)
2019
Cited alongside, same era.
Peng, M., Zhang, Q., Xing, X., Gui, T., Huang, X., Jiang, Y.G., Ding, K., Chen, Z.: Trainable undersampling for class-imbalance learning. In: AAAI (2019)
2019
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
Fan, S., Dong, Q., Zhu, F., Lv, Y., Ye, P., Wang, F.Y.: Scf-net: Learning spatial contextual features for large-scale point cloud segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14504–14513 (June 2021)
2021
Later among the works it cites.
Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Zero-shot detection via vision and language knowledge distillation. arXiv e-prints pp. arXiv–2104 (2021)
2021
Later among the works it cites.
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Perez-Ortiz, M., Tiňo, P., Mantiuk, R., Hervás-Martínez, C.: Exploiting synthetically generated data with semi-supervised learning for small and imbalanced datasets. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 4715–4722 (2019)
2019
Cited alongside, same era.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
Cited alongside, same era.
Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: Kpconv: Flexible and deformable convolution for point clouds. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6411–6420 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Yan, Y., Tan, M., Xu, Y., Cao, J., Ng, M.K., Min, H., Wu, Q.: Oversampling for imbalanced data via optimal transport. In: AAAI (2019)
2019
Cited alongside, same era.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning. pp. 1597–1607. PMLR (2020)
2020
Cited alongside, same era.
Han, L., Zheng, T., Xu, L., Fang, L.: Occuseg: Occupancy-aware 3d instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2940–2949 (2020)
2020
Cited alongside, same era.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9729–9738 (2020)
2020
Cited alongside, same era.
Hou, J., Graham, B., Nießner, M., Xie, S.: Exploring data-efficient 3d scene understanding with contrastive scene contexts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15587–15597 (2021)
2021
Later among the works it cites.
Hsieh, T.I., Robb, E., Chen, H.T., Huang, J.B.: Droploss for long-tail instance segmentation. In: AAAI. vol. 3, p. 15 (2021)
2021
Later among the works it cites.
Huang, S., Xie, Y., Zhu, S.C., Zhu, Y.: Spatio-temporal self-supervised representation learning for 3d point clouds. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6535–6545 (2021)
2021
Later among the works it cites.
Liu, Z., Qi, X., Fu, C.W.: One thing one click: A self-training approach for weakly supervised 3d semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1726–1736 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Nekrasov, A., Schult, J., Litany, O., Leibe, B., Engelmann, F.: Mix3D: Out-of-Context Data Augmentation for 3D Scenes. In: International Conference on 3D Vision (3DV) (2021)
2021
Later among the works it cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. pp. 8748–8763. PMLR (2021)
2021
Later among the works it cites.
Rao, Y., Liu, B., Wei, Y., Lu, J., Hsieh, C.J., Zhou, J.: Randomrooms: Unsupervised pre-training from synthetic shapes and randomized layouts for 3d object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3283–3292 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Wang, C., Ma, C., Zhu, M., Yang, X.: Pointaugmenting: Cross-modal augmentation for 3d object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11794–11803 (2021)
2021
Later among the works it cites.
Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., Pu, S.: Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 16024–16033 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Zhang, Z., Girdhar, R., Joulin, A., Misra, I.: Self-supervised pretraining of 3d features on any point-cloud. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10252–10263 (2021)
2021
Later among the works it cites.