Open Access
SHS Web Conf.
Volume 181, 2024
2023 International Conference on Digital Economy and Business Administration (ICDEBA 2023)
Article Number 03026
Number of page(s) 9
Section Supply Chain Management and Logistics
Published online 17 January 2024
  1. Gorthi, S. S., & Rastogi, P. (2010). Fringe projection techniques: whither we are? Optics and lasers in engineering, 48(2) [Google Scholar]
  2. Qian J., Feng S., Tao T., et al. Deep-learning- enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement]. Apl Photonics, 5, 046105, (2020). [CrossRef] [Google Scholar]
  3. Spoorthi G. E., Gorthi R. K. S. S., Gorthi S., PhaseNet 2.0: Phase unwrapping of noisy data based on deep learning approach[J]. IEEE T Image Process, 29, 4862–4872, (2020). [CrossRef] [Google Scholar]
  4. Yang H., Carlone L. A polynomial-time solution for robust registration with extreme outlier rates[J]. Robotics: Science and Systems, (2019). [Google Scholar]
  5. Zhang L., Chen Q., Zuo C., et al. High-speed high dynamic range 3D shape measurement based on deep learning[J]. Opt Laser Eng, 134: 106245, (2020). [CrossRef] [Google Scholar]
  6. Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. Unpaired image-to-image translation using cycleconsistent adversarial networks. In Proceedings of the IEEE ICCV (pp. 2223–2232) (2017). [Google Scholar]
  7. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. MICCAI 2015: 18th International Conference, Proceedings, Part III 18 pp. 234–241, (2015). [Google Scholar]
  8. Szegedy, Christian, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, vol. 31, no. 1. (2017). [CrossRef] [Google Scholar]
  9. He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CVPR, pp. 770–778. (2016). [Google Scholar]
  10. Glorot, Xavier, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. AISTATS, pp. 315–323. (2011). [Google Scholar]
  11. Lin, Min, Qiang Chen, and Shuicheng Yan. Network in network. arXiv preprint arXiv:1312.4400 (2013). [Google Scholar]
  12. Rota Bulo, Samuel, Gerhard Neuhold, and Peter Kontschieder. “Loss max-pooling for semantic image segmentation.” CVPR, pp. 2126–2135. (2017). [Google Scholar]
  13. Vaswani, Ashish, et al. Attention is all you need. Advances in neural information processing systems 30 (2017). [Google Scholar]
  14. Goodfellow, Ian, et al. Generative adversarial nets. Adv. neural inf. process. syst 27 (2014). [Google Scholar]
  15. Wu, Zhirong, et al. 3d shapenets: A deep representation for volumetric shapes. CVPR. (2015). [Google Scholar]
  16. Wu, Zhirong, et al. 3d shapenets: A deep representation for volumetric shapes. CVPR. (2015). [Google Scholar]
  17. Koch, Sebastian, et al. Abc: A big cad model dataset for geometric deep learning. CVPR. 2019. [Google Scholar]
  18. Zhou, Qingnan, and Alec Jacobson. Thingi10k: A dataset of 10, 000 3d-printing models. arXiv preprint arXiv:1605.04797 (2016). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.