Fetching the paper…
Reading the bibliography…
Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i.e., filling the regions that were missing in the shape.
Smolensky, P.: Information processing in dynamical systems: Foundations of harmony theory. Tech. rep., Colorado Univ at Boulder Dept of Computer Science (1986)
1986
Earlier work this paper cites.
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. science 313
2006
Earlier work this paper cites.
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: International Conference on Machine Learning (ICML). pp. 1096–1103 (2008)
2008
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1912–1920 (2015)
2015
Earlier work this paper cites.
Alec, R., Luke, M., Soumith, C.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: International Conference on Learning Representations (ICLR) (2016)
2016
Earlier work this paper cites.
Sharma, A., Grau, O., Fritz, M.: Vconv-dae: Deep volumetric shape learning without object labels. In: European Conference on Computer Vision (ECCV). pp. 236–250 (2016)
2016
Earlier work this paper cites.
Thanh Nguyen, D., Hua, B.S., Tran, K., Pham, Q.H., Yeung, S.K.: A field model for repairing 3d shapes. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5676–5684 (2016)
2016
Earlier work this paper cites.
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning (ICML). pp. 214–223 (2017)
2017
Earlier work this paper cites.
Dai, A., Ruizhongtai Qi, C., Nießner, M.: Shape completion using 3d-encoder-predictor cnns and shape synthesis. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5868–5877 (2017)
2017
Earlier work this paper cites.
Han, X., Li, Z., Huang, H., Kalogerakis, E., Yu, Y.: High-resolution shape completion using deep neural networks for global structure and local geometry inference. In: International Conference on Computer Vision (ICCV). pp. 85–93 (2017)
2017
Earlier work this paper cites.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4681–4690 (2017)
2017
Earlier work this paper cites.
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: International Conference on Computer Vision (ICCV). pp. 2794–2802 (2017)
2017
Cited alongside, same era.
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 652–660 (2017)
2017
Cited alongside, same era.
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 (NeurIPS). pp. 5099–5108 (2017)
2017
Cited alongside, same era.
Wang, W., Huang, Q., You, S., Yang, C., Neumann, U.: Shape inpainting using 3d generative adversarial network and recurrent convolutional networks. In: International Conference on Computer Vision (ICCV). pp. 2298–2306 (2017)
2017
Cited alongside, same era.
2018
Later among the works it cites.
Yin, K., Huang, H., Cohen-Or, D., Zhang, H.: P2p-net: Bidirectional point displacement net for shape transform. ACM Transactions on Graphics (TOG) 37
2018
Later among the works it cites.
Yu, L., Li, X., Fu, C.W., Cohen-Or, D., Heng, P.A.: Ec-net: an edge-aware point set consolidation network. In: European Conference on Computer Vision (ECCV). pp. 386–402 (2018)
2018
Later among the works it cites.
Yu, L., Li, X., Fu, C.W., Cohen-Or, D., Heng, P.A.: Pu-net: Point cloud upsampling network. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2790–2799 (2018)
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 3391–3401 (2017)
2017
Cited alongside, same era.
Zhu, J.Y., Zhang, R., Pathak, D., Darrell, T., Efros, A.A., Wang, O., Shechtman, E.: Toward multimodal image-to-image translation. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 465–476 (2017)
2017
Cited alongside, same era.
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3d point clouds. In: International Conference on Machine Learning (ICML). pp. 40–49 (2018)
2018
Cited alongside, same era.
Groueix, T., Fisher, M., Kim, V.G., Russell, B., Aubry, M.: AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Cited alongside, same era.
Guerrero, P., Kleiman, Y., Ovsjanikov, M., Mitra, N.J.: Pcpnet learning local shape properties from raw point clouds. In: Computer Graphics Forum. vol. 37, pp. 75–85 (2018)
2018
Cited alongside, same era.
Li, J., Chen, B.M., Hee Lee, G.: So-net: Self-organizing network for point cloud analysis. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9397–9406 (2018)
2018
Cited alongside, same era.
Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: Pointcnn: Convolution on x-transformed points. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 820–830 (2018)
2018
Cited alongside, same era.
Stutz, D., Geiger, A.: Learning 3d shape completion under weak supervision. International Journal of Computer Vision (IJCV) pp. 1–20 (2018)
2018
Cited alongside, same era.
2018
Later among the works it cites.
Zhu, J.Y., Zhang, Z., Zhang, C., Wu, J., Torralba, A., Tenenbaum, J., Freeman, B.: Visual object networks: image generation with disentangled 3d representations. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 118–129 (2018)
2018
Later among the works it cites.
Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5939–5948 (2019)
2019
Later among the works it cites.
Gurumurthy, S., Agrawal, S.: High fidelity semantic shape completion for point clouds using latent optimization. pp. 1099–1108. IEEE (2019)
2019
Later among the works it cites.
Liu, M., Sheng, L., Yang, S., Shao, J., Hu, S.M.: Morphing and sampling network for dense point cloud completion. In: Association for the Advancement of Artificial Intelligence (AAAI) (2019)
2019
Later among the works it cites.
Mo, K., Zhu, S., Chang, A.X., Yi, L., Tripathi, S., Guibas, L.J., Su, H.: PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Later among the works it cites.
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: Learning continuous signed distance functions for shape representation. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 165–174 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Chen, X., Chen, B., Mitra, N.J.: Unpaired point cloud completion on real scans using adversarial training. In: International Conference on Learning Representations (ICLR) (2020)
2020
Closest in time.