Abstract
The establishment of 3D content with deep learning has been a focus of research in computer graphics during past years. Recently, researchers analyze 3D shapes through the dividing-and-conquer strategy with the geometry information and the structure information. Although many works perform well, there are still several problems. For example, the geometry information missing and not plausible in structure. In this work, we propose the Twin-channel GAN for the 3D shape completion. In this framework, the structure information is well studied via the structural constraints for optimizing the details of 3D shapes. The experimental results also demonstrated that our method achieves better performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao: 3d shapenets: A deep representation for vol-umetric shapes. In: Proceedings of the IEEE conferenceon computer vision and pattern recognition, pp. 1912–1920 (2015)
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, Josh Tenenbaum: Learning a probabilistic latent space of object shapesvia 3d generative-adversarial modeling. In: Advances inneural information processing systems, pp. 82–90 (2016)
Gao, L., Yang, J., Tong, W., Yuan, Y.-J., Hongbo, F., Lai, Y.-K., Zhang, H.: Sdm-net: deep generative network for structured deformable mesh. ACM Trans. Graph. 38(6), 1–15 (2019)
Wu, Z., Wang, X., Lin, D., Lischinski, D., Cohen-Or, D., Huang, H.: SAGNet: structure-aware generative network for 3D-shape modeling. ACM Trans. Graph. (Proceedings of SIGGRAPH 2019) 38(4), 91:1–91:14 (2019)
Mo, K., Guerrero, P., Yi, L., Su, H., Wonka, P., Mitra, N.J., Guibas, L.J.L: Structedit: learning structural shape variations. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, 13–19 June 2020, pp. 8856–8865. IEEE (2020)
Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: Proceedings of the Fourth Eurographics Symposium on Geometry Processing, vol. 7 (2006)
Nealen, A., Igarashi, T., Sorkine, O., Alexa, M.: Laplacian mesh optimization. In: Proceedings of the 4th International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia, pp. 381–389 (2006)
Dai, A., Ruizhongtai Qi, C., Nießner, M.: Shape completion using 3d-encoder-predictor cnns and shape synthesis. In: Proceedings of the IEEE international conference on computer vision, pp. 6545–6554 (2017)
Huang, H., Li, Z., He, R., Sun, Z., Tan, T.: Introvae: introspective variational autoencoders for photographic image synthesis. In: Advances in Neural Information Processing Systems, pp. 52–63 (2018)
Goodfellow, I.J., et al.: Generative adversarial networks. Adv. Neural Inf. Process. Syst. 3, 2672–2680 (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: 2nd International Conference on Learning Representations (2014)
Averkiou, M., Kim, V.G., Zheng, Y., Mitra, N.J.: ShapeSynth: parameterizing model collections for coupled shape exploration and synthesis. In: Computer Graphics Forum, vol. 33, pp. 125–134. Wiley Online Library (2014)
Wang, P.-S., Liu, Y., Guo, Y.-X., Sun, C.-Y., Tong, X.: O-CNN: octree-based convolutional neural networks for 3D shape analysis. ACM Trans. Graph. 36(4), 72 (2017)
Wang, J., Fang, Z.: GSIR: generalizable 3D shape interpretation and reconstruction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 498–514. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58601-0_30
Sorkine, O., Cohen-Or, D.: Least-squares meshes. In: Proceedings of the IEEE Shape Modeling Applications, pp. 191–199 (2004)
Mitra, N.J., Guibas, L.J., Pauly, M.: Partial and approximate symmetry detection for 3D geometry. ACM Trans. Graph. 25(3), 560–568 (2006)
Sharma, A., Grau, O., Fritz, M.: VConv-DAE: deep volumetric shape learning without object labels. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 236–250. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_20
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 International Conference on Computer Vision, pp. 1746–1754 (2017)
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3D point clouds. In: International Conference on Machine Learning, pp. 40–49 (2018)
Richard, A., Cherabier, I., Oswald, M.R., Pollefeys, M., Schindler, K.: KAPLAN: a 3D point descriptor for shape completion. In: International Conference on 3D Vision, 3DV 2020, Virtual Event, Japan, 25–28 November 2020, pp. 101–110. IEEE (2020)
Diamond, S., Boyd, S.P.: CVXPY: a python-embedded modeling language for convex optimization. J. Mach. Learn. Res. 17(83), 1–5 (2016)
Acknowledgments
This work is part of the research supported by the Fundamental Research Funds for the Central Universities No. Y03019023601008011, the interactive Technology Research Fund of the Research Center for Interactive Technology Industry, School of Economics and Management, Tsinghua University (No. RCITI2021T006) and sponsored by TiMi L1 Studio of Tencent corporation.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Du, Z., Xie, N., Liu, Z., Zhang, X., Yang, Y. (2021). Twin-Channel Gan: Repair Shape with Twin-Channel Generative Adversarial Network and Structural Constraints. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-89029-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89028-5
Online ISBN: 978-3-030-89029-2
eBook Packages: Computer ScienceComputer Science (R0)