Guorun Yang
Guorun Yang
SenseTime Group Limited
在 sensetime.com 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Segstereo: Exploiting semantic information for disparity estimation
G Yang, H Zhao, J Shi, Z Deng, J Jia
Proceedings of the European Conference on Computer Vision (ECCV), 636-651, 2018
1772018
Drivingstereo: A large-scale dataset for stereo matching in autonomous driving scenarios
G Yang, X Song, C Huang, Z Deng, J Shi, B Zhou
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
402019
Drivable road detection based on dilated FPN with feature aggregation
X Liu, Z Deng, G Yang
2017 IEEE 29th International Conference on Tools with Artificial …, 2017
102017
AdaStereo: a simple and efficient approach for adaptive stereo matching
X Song, G Yang, X Zhu, H Zhou, Z Wang, J Shi
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
92021
Src-disp: Synthetic-realistic collaborative disparity learning for stereo matching
G Yang, Z Deng, H Lu, Z Li
Asian Conference on Computer Vision, 707-723, 2018
42018
Masked label learning for optical flow regression
G Yang, Z Deng, S Wang, Z Li
2018 24th International Conference on Pattern Recognition (ICPR), 1139-1144, 2018
12018
End-to-End Disparity Estimation with Multi-granularity Fully Convolutional Network
G Yang, Z Deng
International Conference on Neural Information Processing, 238-248, 2017
12017
A Computational Model of Match Decision-Making Problem Using Spiking SHESN with Reward-Modulated Reinforcement Learning
Z Deng, G Yang
International Conference on Neural Information Processing, 512-521, 2015
2015
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios–Supplementary Material
G Yang, X Song, C Huang, Z Deng, J Shi, B Zhou
AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching–Supplementary Material
X Song, G Yang, X Zhu, H Zhou, Z Wang, J Shi
SRC-Disp: Synthetic-Realistic Collaborative Disparity Learning for Stereo Matching Download PDF
G Yang, Z Deng, H Lu, Z Li
Masked Label Learning for Optical Flow Regression Download PDF
G Yang, Z Deng, S Wang, Z Li
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