Qingyong Hu

I am a D.Phil student (Oct 2018 - ) in the Department of Computer Science at the University of Oxford, supervised by Profs. Niki Trigoni and Andrew Markham. Prior to Oxford, I obtained my M.Eng degree and B.Eng degree from China and supervised by Profs. Yulan Guo.

Email / Github / Blog / LinkedIn / Twitter / Google Scholar


I am interested in 3D computer vision, machine learning, and robotics. My research goal is to build intelligent systems which are able to achieve an effective and efficient perception and understanding of 3D scenes. In particular, my research focuses on large-scale point cloud segmentation, dynamic point cloud processing, and point cloud tracking. If you are interested in my research or have any use cases that you want to share, feel free to contact me!


[2020.03.08] Invited to present our RandLA-Net and 3D-BoNet at Shenlan. Here are the Video and Slides.

[2020.03.02] The code for our RandLA-Net is available now!

[2020.02.24] Our RandLA-Net is accepted by CVPR2020!

[2020.01.13] Successfully defend D.Phil transfer viva, examined by Profs. Alex Rogers and Profs. Victor Adrian Prisacariu.

[2019.12.27] One co-first authored survey paper on point clouds is on arXiv!

[2019.12.13] Attending and presenting our RandLA-Net on the Turing Data Study Group!

[2019.11.25] Our RandLA-Net is on arXiv!

[2019.09.03] One paper on 3D instance segmentation is accepted as a spotlight at NeurIPS 2019.

Publications / Preprints

Deep Learning for 3D Point Clouds: A Survey
Y. Guo*, H. Wang*, Q. Hu, H. Liu, L. Liu, M. Bennamoun
Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)
arXiv / bibtex / News: (专知, CVer) / Project page

We presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.


RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, A. Markham
Computer Vision and Pattern Recoginition (CVPR), 2020 (Oral, 335/6656)
arXiv / Demo / News: (新智元, 极市平台, AI科技评论) / Code

We propose a simple and efficient neural architecture for 3D semantic segmentation on large-scale point clouds. It achieves the SOTA performance on Semantic3D and SemanticKITTI (Nov 2019), with up to 200x fast than existing approaches.


Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
B. Yang, J. Wang, R. Clark, Q. Hu, S. Wang, A. Markham, N. Trigoni
Neural Information Processing Systems (NeurIPS), 2019 (Spotlight, 200/6743)
arXiv/ Reddit/ Demo/ News: (新智元, 将门创投, 泡泡机器人)/ Code

We propose a simple and efficient neural architecture for accurate 3D instance segmentation on point clouds. It achieves the SOTA performance on ScanNet and S3DIS (June 2019).


Robust Long-term Tracking via Instance Specific Proposals
H. Liu, Q. Hu, B. Li, Y. Guo
IEEE Transactions on Instrumentation and Measurement (IEEE TIM), 2019 (IF=3.06)

We propose an efficient and robust tracker for long-term object tracking, which is based on instance specific proposals. In particular, an instance-specific proposal generator is embedded into the error correction module to recover lost target from tracking failures.


Semi-Online Multiple Object Tracking Using Graphical Tracklet Association
J. Wang, Y. Guo, X. Tang, Q. Hu, W. An
IEEE Signal Processing Letters (IEEE SPL), 2018 (IF=3.27)

We propose a semi-online MOT method using online discriminative appearance learning and tracklet association with a sliding window. We connect similar detections of neighboring frames in a temporal window, and improve the performance of appearance feature by online discriminative appearance learning. Then, tracklet association is performed by minimizing a subgraph decomposition cost.


Object tracking using multiple features and adaptive model updating
Q. Hu, Y. Guo, Z. Lin, W. An, H. Cheng
IEEE Transactions on Instrumentation and Measurement (IEEE TIM), 2018 (IF=3.06)

We proposea to combine a 2-D location filter with a 1-D scale filter to jointly estimate the state of object under tracking, and three complementary features are integrated to further enhance the overall tracking performance. A penalty factor is also defined to achieve a balance between stability and flexibility, especially when the object is under occlusion.


Long-term Object Tracking with Instance Specific Proposals
H. Liu, Q. Hu, B. Li, Y. Guo
24th International Conference on Pattern Recognition (ICPR), 2018

We propose a tracker named Complementary Learners with Instance-specific Proposals (CLIP). The CLIP tracker consists of a translation filter, a scale filter, and an error correction module. The error correction module is activated to correct the localization error by an instance-specific proposal generator, especially when the target suffers from dramatic appearance changes.


Correlation Filter Tracking: Beyond an Open-loop System
Q. Hu, Y. Guo, Y. Chen, J. Xiao, W. An
British Machine Vision Conference (BMVC), 2017
Demo / bibtex / Code

We interpret object tracking as a closed-loop tracking problem, and add a feedback loop to the tracking process by introducing an efficient method to estimate the localization error. We propose a generic self-correction mechanism for CF based trackers by introducing a closed-loop feedback technique.


Robust and real-time object tracking using scale-adaptive correlation filters
Q. Hu, Y. Guo, Z. Lin, X. Deng, W. An
Digital Image Computing: Techniques and Applications (DICTA), 2016 (Oral)

We represent the target in kernel feature space and train a classifier on a scale pyramid to achieve adaptive scale estimation. We then integrate three complementary features to further enhance the overall tracking performance..


Trinity Term, 2019:    Artificial Intelligence (University of Oxford).

Hilary Term, 2020:    Artificial Intelligence (University of Oxford).

Trinity Term, 2020:    Machine Learning (University of Oxford).

Trinity Term, 2020:    Computer Graphics (University of Oxford).

Reviewer Services

Last update: 2020.02.10. Thanks.