Smart Computational Imaging (SCI) Lab

智能计算成像实验室

大学生

南京理工大学

   2012.09-2016.06     光电信息工程                  2012级本科

                                 华中科技大学武昌分校   （现为武昌首义学院）

   2016.09-至今          光学工程                         2016级硕博连读

                                 南京理工大学                  导师：左超（教授，优青）

   1、2017年，“华为杯”第十二届中国研究生电子设计竞赛华东赛区团队二等奖。

   项目：基于条纹投影的三维人脸扫描及识别仪。

   2、2019年-2022年，连续三年入选南京理工大学“优秀博士培养对象”。

   3、2019年，第七届国际光学与光子工程会议（icOPEN2019），优秀报告奖。

   4、2020年，作为团队负责人，获第六届江苏省“互联网+”大学生创新创业大赛江苏省省赛一等奖。

   项目：光影感知——快速三维传感技术助推第四次工业革命。

   5、2020年，获国家留学基金委的国家公派出国留学资格，赴美国普渡大学进行联合培养一年，

        合作导师：Song Zhang（机械工程系教授，SPIE Fellow，OSA Fellow）。

   6、2021年，全国光学与光学工程博士生学术联赛鲁苏皖赛区三等奖，全国百强。

   7、2021年，获江苏省三好学生。

    2019.04   SPIE学生旅行奖学金   

   2020.10   南京理工大学博士国家奖学金

    英语能力       英语六级

    专业能力       基于结构光的光学三维测量，立体视觉，多视觉几何，深度学习，数字散斑相关，点云处理，相机标定，                            系统标定

    计算机能力    Matlab，C/C++和Python编程，多线程，基于CUDA/OpenCL的GPU加速，基于 TensorFlow/PyTorch                          的深度学习框架

期刊论文

1.W. Yin, C.Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen. “High-speedthree-dimensional shape measurement using geometry-constraint-basednumber-theoretical phase unwrapping.” Opticsand Lasers in Engineering, 2019, 115: 21-31.

2.W. Yin, S.Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo. “High-speed 3D shapemeasurement using the optimized composite fringe patterns and stereo-assistedstructured light system.” Optics Express,2019, 27(3): 2411-2431.

3.W. Yin, Q.Chen, S. Feng, T. Tao, L. Huang, M. Trusiak, A. Asundi, and C. Zuo. “Temporalphase unwrapping using deep learning.” ScientificReports, 2019, 9(1): 1-12.

4.W. Yin, S.Feng, T. Tao, L. Huang, S. Zhang, Q. Chen, and C. Zuo. “Calibration method forpanoramic 3D shape measurement with plane mirrors.” Optics Express, 2019, 27(25): 36538-36550. (封面论文).

5.W. Yin, J.Zhong, S. Feng, T. Tao, J. Han, L. Huang, Q. Chen, and C. Zuo. “Composite deeplearning framework for absolute 3D shape measurement based on single fringephase retrieval and speckle correlation.” Journalof Physics: Photonic, 2020, 2(4): 045009.

6.W. Yin, Y.Hu, S. Feng, L. Huang, Q. Kemao, Q. Chen, and C. Zuo. “Single-shot 3D shapemeasurement using an end-to-end stereo matching network for speckle projectionprofilometry,” Optics Express, 2021,29(9): 13388-13407.

7.Y. Hu, Q. Chen, Y. Zhang, S. Feng, T. Tao,H. Li, W. Yin, and C. Zuo.“Dynamic microscopic 3D shape measurement based on marker-embedded Fouriertransform profilometry.” Applied Optics,2018, 54(4): 772-780.

8.C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen. “Phaseshifting algorithms for fringe projection profilometry: A review.” Optics and Lasers in Engineering, 2018,109: 23-59.

9.S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang,Y. Hu, W. Yin, and C. Zuo.“Fringe pattern analysis using deep learning.” Advanced Photonics, 2019, 1(2): 025001. (封面论文).

10.S.Feng, C. Zuo, W. Yin, G. Gu,and Q. Chen. “Micro deep learning profilometry for high-speed 3D surfaceimaging.” Optics and Lasers inEngineering, 2019, 121: 416-427.

11.C. Zuo,X. Zhang, Y. Hu, W. Yin, D.Sheng, J. Zhong, J. Zheng, and Q. Chen. “Has 3D finally come of age?——Anintroduction to 3D structured-light sensor.” Infrared and Laser Engineering, 2020, 49(3): 0303001.

12.S.Feng, C. Zuo, W. Yin, and Q.Chen. “Application of deep learning technology to fringe projection 3Dimaging.” Infrared and Laser Engineering,2020, 49(3): 0303018.

13.J. Zhong, W.Yin, S. Feng, Q. Chen, and C. Zuo. “Speckle projection profilometrywith deep learning.” Infrared and LaserEngineering, 2020, 49(6): 20200011.

14.S. Feng,C. Zuo, L. Zhang, W. Yin, and Q. Chen. “Generalized framework fornon-sinusoidal fringe analysis using deep learning.” Photonics Research, 2021, 9(6): 1084-1098.

15.S. Feng,C. Zuo, L. Zhang, T. Tao, Y. Hu, W. Yin, J. Qian, and Q Chen. “Calibration offringe projection profilometry: A comparative review.” Optics and Lasers in Engineering, 2021, 143: 106622.

会议论文

1.W. Yin, C.Zuo, S. Feng, T. Tao, and Q. Chen. “High-speed 3-D shape measurement usingcomposite structured-light patterns and multi-view system.” Optical Metrologyand Inspection for Industrial Applications V. Vol. 10819. International Societyfor Optics and Photonics, 2018.

2.W. Yin, C.Zuo, S. Feng, T. Tao, and Q. Chen. “High-speed three-dimensional shapemeasurement using improved bi-frequency scheme and number-theoretical phaseunwrapping.” Sixth International Conference on Optical and Photonic Engineering(icOPEN 2018). Vol. 10827. International Society for Optics and Photonics,2018.

3.W. Yin, C.Zuo, S. Feng, T. Tao, and Q. Chen. “Bi-frequency temporal phase unwrappingusing deep learning.” Dimensional Optical Metrology and Inspection forPractical Applications VIII. Vol. 10991. International Society for Optics andPhotonics, 2019.

4.W. Yin, C.Zuo, S. Feng, T. Tao, and Q. Chen. “High-speed 3D shape measurement with the multi-viewsystem using deep learning.” Optical Metrology and Inspection for IndustrialApplications VI. Vol. 11189. International Society for Optics and Photonics,2019.

5.W. Yin, C.Zuo, S. Feng, T. Tao, and Q. Chen. “Temporal phase unwrapping using multi-scaledeep neural networks.” Optical Metrology and Inspection for IndustrialApplications VI. Vol. 11189. International Society for Optics and Photonics,2019.

6.W. Yin, C.Zuo, S. Feng, T. Tao, and Q. Chen. “Fast panoramic 3D shape measurement usingthe multi-view system with plane mirrors.” Seventh International Conference onOptical and Photonic Engineering (icOPEN 2019). Vol. 11205. InternationalSociety for Optics and Photonics, 2019.

7.W. Yin, H.Xu, S. Feng, T. Tao, Q. Chen, and C. Zuo. “System Calibration for Panoramic 3DMeasurement with Plane Mirrors.” International Conference on Image andGraphics. 15-26. Springer, Cham, 2019.

8.W. Yin, C.Zuo, S. Feng, T. Tao, and Q. Chen. “Learning-based absolute 3D shapemeasurement based on single fringe phase retrieval and speckle correlation.”Optics Frontier Online 2020: Optics Imaging and Display. Vol. 11571.International Society for Optics and Photonics, 2020.

9.W. Yin, C.Zuo, S. Feng, T. Tao, and Q. Chen. “An end-to-end speckle matching network for3D imaging.” Optical Metrology and Inspection for Industrial Applications. Vol.11552. International Society for Optics and Photonics, 2020.

10.W. Yin, C.Zuo, S. Feng, T. Tao, and Q. Chen. “An openCL-based speckle matching on themonocular 3D sensor using speckle projection.” Fourth International Conferenceon Photonics and Optical Engineering. Vol. 11761. International Society forOptics and Photonics, 2020.

11.J. Ma, W. Yin, C. Zuo, S. Feng, and Q. Chen. “Real-time binocularstereo vision system based on FPGA.” Sixth International Conference on Opticaland Photonic Engineering (icOPEN 2018). Vol. 10827. International Society forOptics and Photonics, 2018.

12.C. Yang, W. Yin, H. Xu, J. Li, S. Feng, T. Tao, Q. Chen, and C. Zuo.“Single-shot 3D shape measurement with spatial frequency multiplexing usingdeep learning.” Optical Metrology and Inspection for Industrial ApplicationsVI. Vol. 11189. International Society for Optics and Photonics, 2019.

13.Y. Hu, Y. Liang, T. Tao, W. Yin, J. Qian, S. Feng, C.Zuo, and Q. Chen. “Microscopic fringe projection profilometry comparison basedon stereoscopic microscope and telecentric lenses.” Optical Metrology andInspection for Industrial Applications VI. Vol. 11189. International Societyfor Optics and Photonics, 2019.

14.H. Xu, W. Yin, C. Yang, J. Li, S. Feng, T. Tao, Q. Chen, and C.Zuo. “Full-surface 3-D reconstruction based on surround structured lighting.”Seventh International Conference on Optical and Photonic Engineering (icOPEN2019). Vol. 11205. International Society for Optics and Photonics, 2019.

15.Y. Hu, Y. Liang, T. Tao, W. Yin, J. Qian, S. Feng, C.Zuo, and Q. Chen. “High dynamic range and fast 3D measurement based on atelecentric stereo-microscopic system.” Seventh International Conference onOptical and Photonic Engineering (icOPEN 2019). Vol. 11205. InternationalSociety for Optics and Photonics, 2019.

16.Y. Shang, W. Yin, S. Feng, T. Tao, Q. Chen, and C. Zuo. “Fast Stereo3D Imaging Based on Random Speckle Projection and Its FPGA Implementation.”International Conference on Image and Graphics. 205-216. Springer, Cham, 2019.

1.   一种基于深度学习的条纹投影时间相位展开方法，IPC分类号：G01B 11/25，已授权，导师排名第一，本人排名第二。

2.   基于学习的条纹相位恢复和散斑相关的三维测量方法，IPC分类号：G01B 11/25，已受理，本人排名第一。

3.   基于相位级次代价滤波的鲁棒立体相位展开方法，IPC分类号：G01B 11/25，已受理，本人排名第一。

4.   一种基于复合编码的高效立体相位展开方法，IPC分类号：G01B 11/25，已受理，本人排名第一。

5.   一种基于相位信息导向的立体相位展开方法，IPC分类号：G01B 11/25，已受理，本人排名第一。

6.   一种深度图彩色化方法，IPC分类号：G01B 11/25，已受理，本人排名第一。

7.   一种基于OPENCL的单目立体匹配及加速的方法，IPC分类号：G01B 11/25，已受理，本人排名第一。

8.   一种视差图错误点识别与校正方法，IPC分类号：G01B 11/25，已受理，本人排名第一。

9.   一种基于深度学习的端到端散斑投影三维测量方法，IPC分类号：G01B 11/25，已受理，本人排名第一。

10. 一种基于反射镜的条纹投影系统标定方法，IPC分类号：G01B 11/25，已受理，导师排名第一，本人排名第二。

11. 一种用于三维人头模型测量的散斑图案头套设计方法，IPC分类号：G01B 11/25，已受理，导师排名第一，本人排名第二。

12. 基于平面反射镜的360°三维人头测量方法，IPC分类号：G01B 11/25，已受理，导师排名第一，本人排名第二。

13. 基于数字散斑相关的快速三维人头测量方法，IPC分类号：G01B 11/25，已受理，导师排名第一，本人排名第二。