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Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation, Proceedings of International Conference on Computer Vision Conference paper
Wong, C.C., Vong, C. M.. Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation, Proceedings of International Conference on Computer Vision[C], 2021, 7098-7107.
Authors:  Wong, C.C.;  Vong, C. M.
Favorite |  | Submit date：2022/08/09
Persistent Homology  Graph Convolution Network  3D Shape Segmentation  
PU-EVA: An Edge-Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling Conference paper
Luqing Luo, Lulu Tang, Wanyi Zhou, Shizheng Wang, Zhi-Xin Yang. PU-EVA: An Edge-Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling[C]:IEEE, 2021, 16188-16197.
Authors:  Luqing Luo;  Lulu Tang;  Wanyi Zhou;  Shizheng Wang;  Zhi-Xin Yang
Favorite | TC[WOS]:14 TC[Scopus]:27 | Submit date：2022/05/13
Vision For Robotics And Autonomous Vehicles  Low-level And Physics-based Vision , Stereo  3d From Multiview And Other Sensors  
Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation Conference paper
Chi-Chong Wong, Chi-Man Vong. Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation[C]:IEEE, 2021, 7078-7087.
Authors:  Chi-Chong Wong;  Chi-Man Vong
Favorite | TC[WOS]:7 TC[Scopus]:16 | Submit date：2022/05/13
Deep Learning  Point Cloud Compression  Solid Modeling  Three-dimensional Displays  Convolution  Computational Modeling  Semantics  
CrackFormer: Transformer Network for Fine-Grained Crack Detection Conference paper
Liu, Huajun, Miao, Xiangyu, Mertz, Christoph, Xu, Chengzhong, Kong, Hui. CrackFormer: Transformer Network for Fine-Grained Crack Detection[C], IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE, 2021, 3763-3772.
Authors:  Liu, Huajun;  Miao, Xiangyu;  Mertz, Christoph;  Xu, Chengzhong;  Kong, Hui
Favorite | TC[WOS]:80 TC[Scopus]:102 | Submit date：2022/05/13
Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation Conference paper
Jiaming Liu, Ming Lu, Kaixin Chen, Xiaoqi Li, Shizun Wang, Zhaoqing Wang, Enhua Wu, Yurong Chen, Chuang Zhang, Ming Wu. Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation[C], USA:IEEE, 2021, 4611-4620.
Authors:  Jiaming Liu;  Ming Lu;  Kaixin Chen;  Xiaoqi Li;  Shizun Wang; et al.
Favorite | TC[WOS]:18 TC[Scopus]:23 | Submit date：2022/05/13