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Semantic segmentation of 3D meshes is an important problem for 3D scene understanding.
Hermans, A., Floros, G., Leibe, B.: Dense 3d semantic mapping of indoor scenes from rgb-d images. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). pp. 2631–2638. IEEE (2014)
2014
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Lai, K., Bo, L., Fox, D.: Unsupervised feature learning for 3d scene labeling. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). pp. 3050–3057. IEEE (2014)
2014
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Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., Yuille, A.: The role of context for object detection and semantic segmentation in the wild. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2014)
2014
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Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE international conference on computer vision. pp. 945–953 (2015)
2015
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Valentin, J., Vineet, V., Cheng, M.M., Kim, D., Shotton, J., Kohli, P., Nieundefinedner, M., Criminisi, A., Izadi, S., Torr, P.: Semanticpaint: Interactive 3d labeling and learning at your fingertips. In: ACM Transactions on Graphics. ACM (2015)
2015
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Vineet, V., Miksik, O., Lidegaard, M., Nießner, M., Golodetz, S., Prisacariu, V.A., Kähler, O., Murray, D.W., Izadi, S., Pérez, P., et al.: Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). pp. 75–82. IEEE (2015)
2015
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Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3d semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2016)
2016
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Armeni, I., Sax, A., Zamir, A.R., Savarese, S.: Joint 2D-3D-Semantic Data for Indoor Scene Understanding. ArXiv e-prints (Feb 2017)
2017
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Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
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Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5828–5839 (2017)
2017
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Guerry, J., Boulch, A., Le Saux, B., Moras, J., Plyer, A., Filliat, D.: Snapnet-r: Consistent 3d multi-view semantic labeling for robotics. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 669–678 (2017)
2017
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Lawin, F.J., Danelljan, M., Tosteberg, P., Bhat, G., Khan, F.S., Felsberg, M.: Deep projective 3d semantic segmentation. In: International Conference on Computer Analysis of Images and Patterns. pp. 95–107. Springer (2017)
2017
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Ma, L., Stückler, J., Kerl, C., Cremers, D.: Multi-view deep learning for consistent semantic mapping with rgb-d cameras. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 598–605. IEEE (2017)
2017
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McCormac, J., Handa, A., Davison, A., Leutenegger, S.: Semanticfusion: Dense 3d semantic mapping with convolutional neural networks. In: 2017 IEEE International Conference on Robotics and automation (ICRA). pp. 4628–4635. IEEE (2017)
2017
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McCormac, J., Handa, A., Leutenegger, S., Davison, A.J.: Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation? In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2678–2687 (2017)
2017
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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
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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.
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
Cited alongside, same era.
Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: Semantic scene completion from a single depth image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1746–1754 (2017)
2017
Cited alongside, same era.
Tchapmi, L., Choy, C., Armeni, I., Gwak, J., Savarese, S.: Segcloud: Semantic segmentation of 3d point clouds. In: 2017 international conference on 3D vision (3DV). pp. 537–547. IEEE (2017)
2017
Cited alongside, same era.
Chiang, H., Lin, Y., Liu, Y., Hsu, W.H.: A unified point-based framework for 3d segmentation. In: 2019 International Conference on 3D Vision (3DV). pp. 155–163 (Sep 2019)
2019
Later among the works it cites.
Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3075–3084 (2019)
2019
Later among the works it cites.
Choy, C., Park, J., Koltun, V.: Fully convolutional geometric features. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 8958–8966 (2019)
2019
Later among the works it cites.
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., Cohen-Or, D.: Meshcnn: a network with an edge. ACM Transactions on Graphics (TOG) 38
2019
Later among the works it cites.
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Zhang, Y., Song, S., Yumer, E., Savva, M., Lee, J.Y., Jin, H., Funkhouser, T.: Physically-based rendering for indoor scene understanding using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5287–5295 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Boulch, A., Guerry, J., Le Saux, B., Audebert, N.: Snapnet: 3d point cloud semantic labeling with 2d deep segmentation networks. Computers & Graphics 71
2018
Cited alongside, same era.
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV (2018)
2018
Cited alongside, same era.
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
Cited alongside, same era.
Graham, B., Engelcke, M., van der Maaten, L.: 3d semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 9224–9232 (2018)
2018
Cited alongside, same era.
Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Huang, J., Zhang, H., Yi, L., Funkhouser, T., Nießner, M., Guibas, L.J.: Texturenet: Consistent local parametrizations for learning from high-resolution signals on meshes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4440–4449 (2019)
2019
Later among the works it cites.
Jaritz, M., Gu, J., Su, H.: Multi-view pointnet for 3d scene understanding. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 0–0 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Pham, Q.H., Nguyen, T., Hua, B.S., Roig, G., Yeung, S.K.: Jsis3d: joint semantic-instance segmentation of 3d point clouds with multi-task pointwise networks and multi-value conditional random fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8827–8836 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
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 International Conference on Computer Vision. pp. 6411–6420 (2019)
2019
Later among the works it cites.
Valada, A., Mohan, R., Burgard, W.: Self-supervised model adaptation for multimodal semantic segmentation. International Journal of Computer Vision pp. 1–47 (2019)
2019
Later among the works it cites.
Wu, W., Qi, Z., Fuxin, L.: Pointconv: Deep convolutional networks on 3d point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9621–9630 (2019)
2019
Later among the works it cites.
Zhang, C., Liu, Z., Liu, G., Huang, D.: Large-scale 3d semantic mapping using monocular vision. In: 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). pp. 71–76. IEEE (2019)
2019
Later among the works it cites.
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
Closest in time.
Hu, Z., Zhen, M., Bai, X., Fu, H., Tai, C.l.: Jsenet: Joint semantic segmentation and edge detection network for 3d point clouds. In: ECCV (2020)
2020
Closest in time.