Abstract
This article proposes a data-driven methodology to achieve fast design support to generate or develop novel designs covering multiple object categories. This methodology implements two state-of-the-art Variational Autoencoder, dealing with 3D model data, with a self-defined loss function. The loss function, containing the outputs of individual layers in the autoencoder, obtains combinations of different latent features from different 3D model categories. This article provides detail explanation for utilizing the Princeton Model-Net40 database, a comprehensive clean collection of 3D CAD models for objects. After converting the original 3D mesh file to voxel and point cloud data type, the model will feed an autoencoder with data in the same dimension. The novelty is to leverage the power of deep learning methods as an efficient latent feature extractor to explore unknown designing areas. The output is expected to show a clear and smooth interpretation of the model from different categories to generate new shapes. This article will explore (1) the theoretical ideas, (2) the progress to implement Variational Autoencoder to attain implicit features from input shapes, (3) the results of output shapes during training in selected domains of both 3D voxel data and 3D point cloud data, and (4) the conclusion and future work to achieve the more outstanding goal.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Makhzani, A., et al.: Adversarial Autoencoders. American Physical Society, 25 May 2016. http://arxiv.org/abs/1511.05644
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.J.: Representation learning and adversarial generation of 3D point clouds. CoRR, abs/1707.02392 (2017). http://arxiv.org/abs/1707.02392
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A.C., Ben-gio, Y.: A recurrent latent variable model for sequential data. CoRR, abs/1506.02216 (2015). http://arxiv.org/abs/1506.02216
Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. CoRR, abs/1508.06576 (2015). http://arxiv.org/abs/1508.06576
Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Fisher, M.: ShapeNet: an information-rich 3D model repository, 09 December 2015. https://arxiv.org/abs/1512.03012
Goodfellow, J., Pouget-Abadie, J., Mirza, M., Xu, B., Bengio, Y.: Generative Adversarial Networks, 10 June 2014. https://arxiv.org/abs/1406.2661
Huang, X., Belongie, S.J.: Arbitrary style transfer in real-time with adaptive instance normalization. CoRR, abs/1703.06868 (2017). http://arxiv.org/abs/1703.06868
Johnson, J., Alahi, A., Li, F.-F.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision (2016)
Liu, S., Ororbia II, A.G., Giles, C.L.: Learning a hierarchical latent-variable model of voxelized 3D shapes. CoRR, abs/1705.05994 (2017). http://arxiv.org/abs/1705.05994
Rezende, D.J., Eslami, S.M.A., Mohamed, S., Battaglia, P., Jaderberg, M., Heess, N.: Unsupervised learning of 3D structure from images. CoRR, abs/1607.00662 (2016). http://arxiv.org/abs/1607.00662
Wu, Z., Song, S., Khosla, A., Tang, X., Xiao, J.: 3D shapenets for 2.5D object recognition and next-best-view prediction. CoRR, abs/1406.5670 (2014). http://arxiv.org/abs/1406.5670
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, Z., Jiang, H., Zou, L. (2020). 3D Conceptual Design Using Deep Learning. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-17795-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17794-2
Online ISBN: 978-3-030-17795-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)