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20180425-10 谢晋:Deep learning based 3D shape representation

2018-4-20 01:43| 发布者: 程一-计算所| 查看: 3390| 评论: 0

摘要: 报告嘉宾:谢晋(南京理工大学计算机学院)报告时间:2018年04月25日(星期三)晚上20:00(北京时间)报告题目:Deep learning based 3D shape representation主持人:张林(同济大学)报告人简介:Jin Xie received ...



报告题目:Deep learning based 3D shape representation



Jin Xie received his Ph.D. degree from the Department of Computing, The Hong Kong Polytechnic University. He is a professor at Nanjing University of Science and Technology (NJUST), China. Prior to joining NJUST, he was a research scientist at New York University Abu Dhabi and New York University Tandon School of Engineering. His research interests include image forensics, computer vision and machine learning. Currently he is focusing on 3D computer vision with convex optimization and deep learning methods, including 3D shape analysis, 3D object detection and 3D scene understanding. He has published papers in top conferences and journals, including CVPR, ECCV, AAAI, ACM MM, IEEE TPAMI and TIP. He has served as a PC member for CVPR, ICCV, ECCV, ACM MM, ICPR and ACPR, a journal reviewer for IEEE TPAMI, TIP, TNNLS, TMM, TCYB, TCSVT, PR and PRL. He was a special issue chair for ACPR 2017 and a guest editor for Pattern Recognition.



1.Learning Barycentric representations of 3D shapes for sketch-based 3D shape retrieval, Jin Xie, Guoxian Dai, Fan Zhu and Yi Fang,CVPR, 2017.

2.Learned binary spectral shape descriptor for 3D shape correspondence, Jin Xie, Meng Wang and Yi Fang, CVPR, 2016.

3.Heat diffusion long-short term memory learning for 3D shape analysis, Fan Zhu, Jin Xie and Yi Fang, ECCV, 2016.

4.Deepshape:deep learned shape descriptor for 3D shape matching and retrieval, Jin Xie, Yi Fang, Fan Zhu and Edward K.Wong, CVPR, 2015.


Advances in deep learning via deep neural networks have resulted in great gains in the computer vision community. Different from 2D images, 3D shapes do not contain rich textures and colors, but contain geometric structures. How to represent 3D shapes with deep neural networks is a challenging problem. In this talk, I will present deep learning based 3D shape representations and their applications in 3D shape analysis. First, I will introduce the heat diffusion theory in 3D shape analysis. Based on the heat diffusion theory, I will then present 3D shape feature extraction with deep neural networks for 3D shape retrieval and correspondence. In addition, the potential applications of 3D shape representations will also be discussed.





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