METHODS AND SYSTEMS FOR GENERATING 3D DATASETS TO TRAIN DEEP LEARNING NETWORKS FOR MEASUREMENTS ESTIMATION

• 1. 生成深度学习目标检测网络训练用带标注数据集的方法 [P] . 中国专利： CN109816014A . 2019-05-28
• 2. 用于深度学习地铁轨道识别的训练数据集标注的生成方法 [P] . 中国专利： CN109492519A . 2019-03-19
• 3. METHODS AND SYSTEMS FOR GENERATING 3D DATASETS TO TRAIN DEEP LEARNING NETWORKS FOR MEASUREMENTS ESTIMATION [P] . WO2021087425A1 . 2021-05-06

  机译： 用于生成3D数据集以培训深度学习网络进行测量估计的方法和系统

• 4. Methods and systems for generating 3D datasets to train deep learning networks for measurements estimation [P] . US11010896B2 . 2021-05-18

  机译： 用于生成3D数据集以培训深度学习网络进行测量估计的方法和系统

• 5. Systems and methods for full body measurements extraction using multiple deep learning networks for body feature measurements [P] . 美国专利： US10470510B1 . 2019-11-12

  机译： 使用多个深度学习网络进行人体特征测量的全身测量提取系统和方法

• 1. Learning a Deep Predictive Coding Network for a Semi-Supervised 3D-Hand Pose Estimation [J] . Jamal Banzi, Isack Bulugu, Zhongfu Ye 自动化学报：英文版 . 2020,第005期
• 2. Learning a Deep Predictive Coding Network for a Semi-Supervised 3D-Hand Pose Estimation [J] . Jamal Banzi, Isack Bulugu, Zhongfu Ye 自动化学报（英文版） . 2020,第005期
• 3. Channel Estimation with an Interpolation Trained Deep Neural Network [J] . Yu Hu, Jianing Zhao, Bingyang Cheng 电脑和通信（英文） . 2021,第010期
• 4. Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging [J] . Fu Jie, Yang Yingli, Singhrao Kamal, Medical Physics . 2019,第9期
• 5. Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks [J] . Zhang Tuan-Feng, Tilke Peter, Dupont Emilien, Annals of the American Thoracic Society . 2019,第3期
• 6. Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks [J] . Tuan-Feng Zhang, Peter Tilke, Emilien Dupont, Petroleum science . 2019,第3期
• 7. Machine Learning and Deep Neural Network Architectures for 3D Motion Capture Datasets [C] . Alistair Boyle, Gwyneth B. Ross, Ryan B. Graham Annual International Conference of the IEEE Engineering in Medicine and Biology Society . 2020
• 8. Leveraging Pre-Trained Detection Networks for Label Generation on New Datasets [D] . Walsh, Sean. 2020
• 9. Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation Photometric Conversion or Transfer Learning [O] . Vikash Gupta, Mutlu Demirer, Matthew Bigelow, 2020
• 10. Performance of Ensemble Methods with 2D Pre-trained Deep Learning Networks for 3D MRI Brain Segmentation [O] . Sang-il Ahn, Toan Duc Bui, Hyekyoung Hwang, 2019

• 1. Deep Learning Based Channel Estimation in Fog Radio Access Networks [J] . Zhendong Mao ,Shi Yan . 中国通信 . 2019,第011期
• 2. The Measuring System for Estimation of Power of Wind Flow Generated by Train Movement and Its Experimental Testing [J] . Oleksandr Mokin ,Borys Mokin ,Vadym Bazalytskyy . 能源与动力工程（英文） . 2014,第11期
• 3. 一种用于动态流量软测量系统的神经网络训练方法 [C] . 唐勇 ,王益群 ,姜万录 . 第3届全国流体传动及控制学术会议 . 2004
• 4. 面向创意3D生成的深度学习方法研究 [A] . 李康 . 2020