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3D reverse engineering, in which a CAD model is inferred given a 3D scan of a physical object, is a research direction that offers many promising practical applications.
Perlin, K.: An image synthesizer. In: SIGGRAPH. p. 287–296. SIGGRAPH ’85, Association for Computing Machinery, New York, NY, USA (1985)
1985
Earlier work this paper cites.
Lim, J.J., Pirsiavash, H., Torralba, A.: Parsing ikea objects: Fine pose estimation. In: CVPR. pp. 2992–2999 (2013)
2013
Earlier work this paper cites.
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial networks. NeurIPS (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. NeurIPS 29
2016
Earlier work this paper cites.
Li, J., Xu, K., Chaudhuri, S., Yumer, E., Zhang, H., Guibas, L.: Grass: Generative recursive autoencoders for shape structures. ACM TOG (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. NeurIPS 30
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. NeurIPS 30
2017
Earlier work this paper cites.
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3d point clouds. In: ICML. pp. 40–49. PMLR (2018)
2018
Earlier work this paper cites.
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.G.: Pixel2mesh: Generating 3d mesh models from single rgb images. In: ECCV. pp. 52–67 (2018)
2018
Earlier work this paper cites.
Yu, L., Li, X., Fu, C.W., Cohen-Or, D., Heng, P.A.: Ec-net: an edge-aware point set consolidation network. In: ECCV. pp. 386–402 (2018)
2018
Earlier work this paper cites.
Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: CVPR. pp. 5939–5948 (2019)
2019
Earlier work this paper cites.
Friedrich, M., Fayolle, P.A., Gabor, T., Linnhoff-Popien, C.: Optimizing evolutionary csg tree extraction. In: Proceedings of the Genetic and Evolutionary Computation Conference. pp. 1183–1191 (2019)
2019
Earlier work this paper cites.
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: CVPR. pp. 9601–9611 (2019)
2019
Earlier work this paper cites.
Li, L., Sung, M., Dubrovina, A., Yi, L., Guibas, L.J.: Supervised fitting of geometric primitives to 3d point clouds. In: CVPR. pp. 2652–2660 (2019)
2019
Earlier work this paper cites.
Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: Pointflow: 3d point cloud generation with continuous normalizing flows. In: CVPR. pp. 4541–4550 (2019)
2019
Earlier work this paper cites.
Cai, R., Yang, G., Averbuch-Elor, H., Hao, Z., Belongie, S., Snavely, N., Hariharan, B.: Learning gradient fields for shape generation. In: ECCV (2020)
2020
Earlier work this paper cites.
Carlier, A., Danelljan, M., Alahi, A., Timofte, R.: Deepsvg: A hierarchical generative network for vector graphics animation. NeurIPS 33
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. NeurIPS (2020)
2020
Cited alongside, same era.
Kania, K., Zieba, M., Kajdanowicz, T.: Ucsg-net-unsupervised discovering of constructive solid geometry tree. NeurIPS (2020)
2020
Cited alongside, same era.
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: CVPR (2020)
2020
Cited alongside, same era.
Nash, C., Ganin, Y., Eslami, S.A., Battaglia, P.: Polygen: An autoregressive generative model of 3d meshes. In: ICML. pp. 7220–7229. PMLR (2020)
2020
Cited alongside, same era.
Sharma, G., Liu, D., Maji, S., Kalogerakis, E., Chaudhuri, S., Měch, R.: Parsenet: A parametric surface fitting network for 3d point clouds. In: ECCV. pp. 261–276. Springer (2020)
2020
Cited alongside, same era.
Ren, D., Zheng, J., Cai, J., Li, J., Zhang, J.: Extrudenet: Unsupervised inverse sketch-and-extrude for shape parsing. In: ECCV. pp. 482–498. Springer (2022)
2022
Later among the works it cites.
Uy, M.A., Chang, Y.Y., Sung, M., Goel, P., Lambourne, J.G., Birdal, T., Guibas, L.J.: Point2cyl: Reverse engineering 3d objects from point clouds to extrusion cylinders. In: CVPR. pp. 11850–11860 (2022)
2022
Later among the works it cites.
Xu, X., Willis, K.D., Lambourne, J.G., Cheng, C.Y., Jayaraman, P.K., Furukawa, Y.: Skexgen: Autoregressive generation of cad construction sequences with disentangled codebooks. In: ICML. pp. 24698–24724. PMLR (2022)
2022
Later among the works it cites.
Cherenkova, K., Dupont, E., Kacem, A., Arzhannikov, I., Gusev, G., Aouada, D.: Sepicnet: Sharp edges recovery by parametric inference of curves in 3d shapes. In: CVPRW. pp. 2726–2734 (2023)
2023
Later among the works it cites.
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Wang, X., Xu, Y., Xu, K., Tagliasacchi, A., Zhou, B., Mahdavi-Amiri, A., Zhang, H.: Pie-net: Parametric inference of point cloud edges. NeurIPS 33
2020
Cited alongside, same era.
Wu, R., Zhuang, Y., Xu, K., Zhang, H., Chen, B.: Pq-net: A generative part seq2seq network for 3d shapes. In: CVPR (2020)
2020
Cited alongside, same era.
Jayaraman, P.K., Sanghi, A., Lambourne, J.G., Willis, K.D., Davies, T., Shayani, H., Morris, N.: Uv-net: Learning from boundary representations. In: CVPR. pp. 11703–11712 (2021)
2021
Cited alongside, same era.
Lambourne, J.G., Willis, K.D., Jayaraman, P.K., Sanghi, A., Meltzer, P., Shayani, H.: Brepnet: A topological message passing system for solid models. In: CVPR. pp. 12773–12782 (2021)
2021
Cited alongside, same era.
Liu, Y., D’Aronco, S., Schindler, K., Wegner, J.D.: Pc2wf: 3d wireframe reconstruction from raw point clouds. ICLR (2021)
2021
Cited alongside, same era.
Willis, K.D., Pu, Y., Luo, J., Chu, H., Du, T., Lambourne, J.G., Solar-Lezama, A., Matusik, W.: Fusion 360 gallery: A dataset and environment for programmatic cad construction from human design sequences. ACM TOG 40
2021
Cited alongside, same era.
Wu, R., Xiao, C., Zheng, C.: Deepcad: A deep generative network for computer-aided design models. In: CVPR. pp. 6772–6782 (2021)
2021
Cited alongside, same era.
Jayaraman, P.K., Lambourne, J.G., Desai, N., Willis, K.D., Sanghi, A., Morris, N.J.: Solidgen: An autoregressive model for direct b-rep synthesis. Transaction in Machine Learning Research (2023)
2023
Later among the works it cites.
Li, P., Guo, J., Zhang, X., Yan, D.M.: Secad-net: Self-supervised cad reconstruction by learning sketch-extrude operations. In: CVPR. pp. 16816–16826 (2023)
2023
Later among the works it cites.
Ma, W., Xu, M., Li, X., Zhou, X.: Multicad: Contrastive representation learning for multi-modal 3d computer-aided design models. In: CIKM. Association for Computing Machinery, New York, NY, USA (2023)
2023
Later among the works it cites.
Mallis, D., Aziz, A.S., Dupont, E., Cherenkova, K., Karadeniz, A.S., Khan, M.S., Kacem, A., Gusev, G., Aouada, D.: Sharp challenge 2023: Solving cad history and parameters recovery from point clouds and 3d scans. overview, datasets, metrics, and baselines. In: CVPRW (2023)
2023
Later among the works it cites.
Xu, X., Jayaraman, P.K., Lambourne, J.G., Willis, K.D., Furukawa, Y.: Hierarchical neural coding for controllable cad model generation. ICML (2023)
2023
Later among the works it cites.
Zhu, X., Du, D., Chen, W., Zhao, Z., Nie, Y., Han, X.: Nerve: Neural volumetric edges for parametric curve extraction from point cloud. In: CVPR. pp. 13601–13610 (2023)
2023
Later among the works it cites.
Khan, M.S., Dupont, E., Ali, S.A., Cherenkova, K., Kacem, A., Aouada, D.: Cad-signet: Cad language inference from point clouds using layer-wise sketch instance guided attention. In: CVPR. pp. 4713–4722 (2024)
2024
Closest in time.
Ma, W., Chen, S., Lou, Y., Li, X., Zhou, X.: Draw step by step: Reconstructing cad construction sequences from point clouds via multimodal diffusion. In: CVPR. pp. 27154–27163 (2024)
2024
Closest in time.
Paviot, T.: Pythonocc (2017), https://dev.opencascade.org/project/pythonocc , accessed: March 7, 2024
2024
Closest in time.
Team, O.D.: Open3d documentation: open3d.geometry.estimate_normals (2022), https://www.open3d.org/docs/0.7.0/python_api/open3d.geometry.estimate_normals.html , accessed: March 6, 2024
2024
Closest in time.
Wijmans, E.: Pointnet++ pytorch (2018), https://github.com/erikwijmans/Pointnet2_PyTorch , accessed: March 7, 2024
2024
Closest in time.
Xu, X., Lambourne, J.G., Jayaraman, P.K., Wang, Z., Willis, K.D., Furukawa, Y.: Brepgen: A b-rep generative diffusion model with structured latent geometry. ACM SIGGRAPH (2024)
2024
Closest in time.
Yu, F., Chen, Q., Tanveer, M., Mahdavi Amiri, A., Zhang, H.: D2csg: Unsupervised learning of compact csg trees with dual complements and dropouts. Advances in Neural Information Processing Systems 36
2024
Closest in time.