Linqing Zhao

Linqing Zhao（赵林清）

I'm currently a Postdoctoral fellow of Department of Automation, Tsinghua University, affiliated with Intelligent Vision Group (IVG), supervised by Prof. Jiwen Lu.

I received the B.Eng. and Ph.D. degrees in information and communication engineering from the School of Electrical and Information Engineering, Tianjin University, China, in 2017 and 2024, respectively, supervised by Prof. Zhanjie Song. During my PhD studies, I was honored to be a visiting student at Intelligent Vision Group (IVG), supervised by Prof. Jie Zhou and Prof. Jiwen Lu.

My research interests lie in computer vision, especially robot vision, autonomous driving perception, and deep learning.

Email / Google Scholar / Github / 中文版简历

News

2024-05: One paper on Lane Detection is accepted to TIP.

2024-02: Two papers on 3D Occupancy and Online 3D Scene Perception are accepted to CVPR 2024.

2024-01: One paper on Depth Completion is accepted to TIP.

2023-09: One paper on Unsupervised Depth Completion is accepted to TCSVT.

Recent Selected Publications

(*Equal Contribution, #Corresponding Author)

	StructLane: Leveraging Structural Relations for Lane Detection Linqing Zhao, Wenzhao Zheng, Yunpeng Zhang, Jie Zhou, Jiwen Lu# IEEE Transactions on Image Processing (TIP), 2024. We propose the StructLane method to enhance lane detection accuracy and robustness by harnessing the structural relationships among lanes.
	LowRankOcc: Tensor Decomposition and Low-Rank Recovery for Vision-based 3D Semantic Occupancy Prediction Linqing Zhao, Xiuwei Xu, Ziwei Wang, Yunpeng Zhang, Borui Zhang, Wenzhao Zheng, Dalong Du, Jie Zhou, Jiwen Lu# IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. We propose LowRankOcc to address spatial redundancy in 3D semantic occupancy prediction, leveraging the inherent low-rank property of occupancy data.
	Structure-aware Cross-Modal Transformer for Depth Completion Linqing Zhao, Yi Wei, Jiaxin Li, Jie Zhou, Jiwen Lu# IEEE Transactions on Image Processing (TIP), 2024. We disentangle the hierarchical 3D scene-level structure from the RGB-D input and construct a pathway to make sharp depth boundaries and object shape outlines accessible to 2D features.
	SPTR: Structure-Preserving Transformer for Unsupervised Indoor Depth Completion Linqing Zhao, Wenzhao Zheng, Yueqi Duan, Jie Zhou, Jiwen Lu# IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2023. We propose to reformulate depth completion as the process of 3D structure generation, where the generated structure should recover the complete scene and also consist with the known partial structure.
	SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving Yi Wei, Linqing Zhao*, Wenzhao Zheng, Zheng Zhu, Jie Zhou , Jiwen Lu# IEEE International Conference on Computer Vision (ICCV), 2023* We design a pipeline to generate dense occupancy ground truths without expensive occupancy annotations, which enables the training of more dense 3D occupancy prediction models.
	Dense Hybrid Proposal Modulation for Lane Detection Yuejian Wu, Linqing Zhao, Jiwen Lu, Haibin Yan# IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2023. We densely modulate all proposals to generate topologically and spatially high-quality lane predictions with discriminative representations.
	SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation Yi Wei, Linqing Zhao*, Wenzhao Zheng, Zheng Zhu, Yongming Rao, Guan Huang, Jiwen Lu#, Jie Zhou Conference on Robot Learning (CoRL), 2022* We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras.
	Learning Hybrid Semantic Affinity for Point Cloud Segmentation Zhanjie Song, Linqing Zhao, Jie Zhou# IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2021. We present a hybrid semantic affinity learning method (HSA) to capture and leverage the dependencies of categories for 3D semantic segmentation, which aims to learn the label dependencies between 3D points from a hybrid perspective.
	Similarity-Aware Fusion Network for 3D Semantic Segmentation Linqing Zhao, Jiwen Lu#, Jie Zhou IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021. We propose a similarity-aware fusion network (SAFNet) to adaptively fuse 2D images and 3D point clouds for 3D semantic segmentation.

Honors

Academic Scholarship of Tianjin University: 2017, 2019, 2022

First Prize of the Doctoral Student Academic Forum of the School of EIE, Tianjin University: 2022

Academic Services

Conference Reviewer: ICRA, IROS, ACM MM, ICME, ICASSP

Journal Reviewer: TIP, TCSVT, TBIOM, Pattern Recognition

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