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The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years.
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Huang, H., Kalogerakis, E., Chaudhuri, S., Ceylan, D., Kim, V.G., Yumer, E.: Learning local shape descriptors from part correspondences with multiview convolutional networks. ACM Transactions on Graphics 37
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Larsson, G., Maire, M., Shakhnarovich, G.: Learning representations for automatic colorization. In: European Conference on Computer Vision. pp. 577–593. Springer (2016)
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Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2536–2544 (2016)
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Sharma, A., Grau, O., Fritz, M.: Vconv-dae: Deep volumetric shape learning without object labels. In: European Conference on Computer Vision. pp. 236–250. Springer (2016)
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Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In: Advances in neural information processing systems. pp. 82–90 (2016)
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Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European conference on computer vision. pp. 649–666. Springer (2016)
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2018
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Li, J., Chen, B.M., Hee Lee, G.: So-net: Self-organizing network for point cloud analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 9397–9406 (2018)
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Muralikrishnan, S., Kim, V.G., Chaudhuri, S.: Tags2Parts: Discovering semantic regions from shape tags. In: Proc. CVPR. IEEE (2018)
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Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.H., Kautz, J.: SPLATNet: Sparse lattice networks for point cloud processing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
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Su, J.C., Gadelha, M., Wang, R., Maji, S.: A deeper look at 3d shape classifiers. In: Proceedings of the European Conference on Computer Vision Workshops (ECCV) (2018)
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Wang, P.S., Sun, C.Y., Liu, Y., Tong, X.: Adaptive o-cnn: A patch-based deep representation of 3d shapes. ACM Trans. Graph. 37
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Yang, Y., Feng, C., Shen, Y., Tian, D.: Foldingnet: Point cloud auto-encoder via deep grid deformation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 206–215 (2018)
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Caron, M., Bojanowski, P., Mairal, J., Joulin, A.: Unsupervised pre-training of image features on non-curated data. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2959–2968 (2019)
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Chen, Z., Tagliasacchi, A., Zhang, H.: Bsp-net: Generating compact meshes via binary space partitioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
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Chen, Z., Yin, K., Fisher, M., Chaudhuri, S., Zhang, H.: Bae-net: branched autoencoder for shape co-segmentation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 8490–8499 (2019)
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Donahue, J., Simonyan, K.: Large scale adversarial representation learning. In: Advances in Neural Information Processing Systems. pp. 10541–10551 (2019)
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Hassani, K., Haley, M.: Unsupervised multi-task feature learning on point clouds. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 8160–8171 (2019)
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2019
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Sharma, G., Kalogerakis, E., Maji, S.: Learning point embeddings from shape repositories for few-shot segmentation. In: 2019 International Conference on 3D Vision (3DV). pp. 67–75 (2019)
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Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (TOG) 38
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Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: Pointflow: 3d point cloud generation with continuous normalizing flows. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 4541–4550 (2019)
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Zhao, Y., Birdal, T., Deng, H., Tombari, F.: 3d point capsule networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1009–1018 (2019)
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Deng, B., Genova, K., Yazdani, S., Bouaziz, S., Hinton, G., Tagliasacchi, A.: Cvxnet: Learnable convex decomposition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)
2020
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