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We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives.
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Sorkine, O., Alexa, M.: As-rigid-as-possible surface modeling. In: Symposium on Geometry Processing. pp. 109–116 (2007)
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Yumer, M.E., Kara, L.B.: Surface creation on unstructured point sets using neural networks. Computer-Aided Design
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Schulman, J., Heess, N., Weber, T., Abbeel, P.: Gradient estimation using stochastic computation graphs. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2. p. 3528–3536. NIPS’15, MIT Press, Cambridge, MA, USA (2015)
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Tulsiani, S., Su, H., Guibas, L.J., Efros, A.A., Malik, J.: Learning shape abstractions by assembling volumetric primitives. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)
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Kaiser, A., Ybanez Zepeda, J.A., Boubekeur, T.: A survey of simple geometric primitives detection methods for captured 3D data. Computer Graphics Forum
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Koch, S., Matveev, A., Jiang, Z., Williams, F., Artemov, A., Burnaev, E., Alexa, M., Zorin, D., Panozzo, D.: ABC: A big cad model dataset for geometric deep learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
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Li, L., Sung, M., Dubrovina, A., Yi, L., Guibas, L.J.: Supervised fitting of geometric primitives to 3d point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
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Paschalidou, D., Ulusoy, A.O., Geiger, A.: Superquadrics revisited: Learning 3d shape parsing beyond cuboids. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10336–10345 (2019)
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Zou, C., Yumer, E., Yang, J., Ceylan, D., Hoiem, D.: 3d-prnn: Generating shape primitives with recurrent neural networks. In: The IEEE International Conference on Computer Vision (ICCV) (2017)
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Kong, S., Fowlkes, C.: Recurrent pixel embedding for instance grouping. In: 2018 Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
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Sharma, G., Goyal, R., Liu, D., Kalogerakis, E., Maji, S.: Csgnet: Neural shape parser for constructive solid geometry. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 5515–5523 (2018)
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2019
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Cuturi, M., Teboul, O., Vert, J.P.: Differentiable ranking and sorting using optimal transport. In: Advances in Neural Information Processing Systems 32 (2019)
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Sun, C., Zou, Q., Tong, X., Liu, Y.: Learning adaptive hierarchical cuboid abstractions of 3D shape collections. ACM Transactions on Graphics (SIGGRAPH Asia)
2019
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Tian, Y., Luo, A., Sun, X., Ellis, K., Freeman, W.T., Tenenbaum, J.B., Wu, J.: Learning to infer and execute 3d shape programs. In: International Conference on Learning Representations (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
2019
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Paschalidou, D., Gool, L.V., Geiger, A.: Learning unsupervised hierarchical part decomposition of 3d objects from a single rgb image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
Closest in time.
Smirnov, D., Fisher, M., Kim, V.G., Zhang, R., Solomon, J.: Deep parametric shape predictions using distance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
Closest in time.