About Me

I am currently a second-year Ph.D. student in Intelligent Sensing, Perception and Computing (ISPC) Group of Tongji University and supervised by Prof. Guang Chen. Before that, I received my bachelor's degree of Automotive Engineering in Tongji University in 2020. My research interests mainly focus on 3D Computer Vision, Deep Learning, SLAM, Autonomous Driving. My CV can be downloaded from here.

News
  • [2021/07] One paper got accepted by ICCV 2021.
  • [2020/12] One paper got accepted by AAAI 2021.
  • [2020/09] One paper got accepted by NeurIPS 2020.
Education
  • Tongji University (2015-2020)
  • Bachelor's Degree in Automotive Engineering
Experiences
  • JD Explore Academy
  • Research Intern
Publications
HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
Fan Lu, Guang Chen*, Yinlong Liu, Lijun Zhang, Sanqing Qu, Shu Liu, Rongqi Gu
International Conference on Computer Vision (ICCV), 2021
[Arxiv] [Pdf] [Code]
This paper proposes a hierarchical network named HRegNet, which leverages rich features in deeper layer and precise location information of keypoints in shallower layers for robust and accurate LiDAR point cloud registration. Bilateral consensus and neighborhood consensus are utilized to improve the robustness of the keypoints matching. Extensive experiments on KITTI dataset and NuScenes dataset demonstrate the good performance of the proposed HRegNet.
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
Fan Lu, Guang Chen*, Yinlong Liu, Zhongnan Qu, Alois Knoll
Advances in Neural Information Processing Systems (NeurIPS), 2020
[Paper] [Code]
This paper proposes Random Sample-based Keypoint Detector and Descriptor Network (RSKDD-Net) for large scale point cloud registration. The key idea is using random sampling to efficiently select candidate points and using a learning-based method to jointly generate keypoints and descriptors.
PointINet: Point Cloud Frame Interpolation Network
Fan Lu, Guang Chen*, Sanqing Qu, Zhijun Li, Yinlong Liu, Alois Knoll
Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
[Paper] [Code]
We propose a novel framework, namely Point Cloud Frame Interpolation Network (PointINet). Based on the proposed method, the low frame rate point cloud streams can be upsampled to higher frame rates.
Pre-Prints
MoNet: Motion-based Point Cloud Prediction Network
Fan Lu, Guang Chen*, Yinlong Liu, Zhijun Li, Sanqing Qu, Tianpei Zou
[Paper]
Honors and Awards
  • Shanghai Outstanding Graduate, 2020
  • Second Prize of National Post-Graduate Mathematical Contest in Modeling, 2020