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

Research

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, point cloud tracking, and local surface matching. If you are interested in my research or have any use cases that you want to share, feel free to contact me!

News

[2021.05.15] Our RandLA-Net has been extended to the IEEE TPAMI 2021!

[2021.03.01] Two papers (SensatUrban, SpinNet) are accepted to CVPR 2021!

[2021.01.30] One paper on efficient semantic segmentation is accepted to ICASSP 2021!

[2020.11.25] Our SpinNet (3D point cloud registration) is on arXiv!

[2020.10.15] our RandLA-Net has been integrated to Open3d as a standard module!

[2020.10.09] Invited to present our work on Wonderland AI Summit 2020!

[2020.09.22] Invited to present our recent works on 2nd Workshop on 3D-DLAD at IEEE Intelligent Vehicles 2020!

[2020.09.22] We will organize a tutorial of 3D Point Cloud Reconstruction and Segmentation in the 3DV 2020!

[2020.09.08] Our SensatUrban (Urban-scale photogrammetric point cloud) dataset is on arXiv!

[2020.08.05] One paper on CNN intepretation is accepted as oral presentation at BMVC2020!

[2020.06.23] Our point cloud survey paper has been accepted to IEEE TPAMI!

[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
PontTuset

Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling
Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, A. Markham
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 (IF=17.73)
Paper / Demo / Code

This is a journal version of the RandLA-Net, with more extensive experiments on the Toronto3D, NPM3D, ScanNet and DALES. We provide more details regarding the entire architecture and the implementation of our RandLA-Net, including all specifics of encoding/decoding neural layers and particulars of the training strategy.

PontTuset

SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000× Fewer Labels
Q. Hu, B. Yang, G. Fang, A. Leornadis, Y. Guo, N. Trigoni, A. Markham
Arxiv, 2021
arXiv / Demo / Project Page / Code

We propose a new weak supervision method to implicitly augment the total amount of available supervision signals, by leveraging the semantic similarity between neighboring points. Extensive experiments demonstrate that the proposed Semantic Query Net- work (SQN) achieves state-of-the-art performance on six large-scale open datasets under weak supervision schemes, while requiring only 1‰ labeled points for training.

PontTuset

SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
S. Ao*, Q. Hu*, B. Yang, A. Markham, Y. Guo
CVPR, 2021 (* indicates equal contribution)
arXiv / Demo / Project Page / Code

We introduce a new, yet conceptually simple, neural architecture to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching.

PontTuset

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
Q. Hu, B. Yang, S. Khalid, W. Xiao, N. Trigoni, A. Markham
CVPR, 2021
arXiv / Demo / Project Page / Code

We introduce an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is five times the number of labeled points than the existing largest point cloud dataset. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular, we identify several key challenges towards urban-scale point cloud understanding.

PontTuset

Deep Learning for 3D Point Clouds: A Survey
Y. Guo*, H. Wang*, Q. Hu*, H. Liu*, L. Liu, M. Bennamoun
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 (IF=17.73)
(* indicates equal contribution)
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.

PontTuset

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.

PontTuset

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).

PontTuset

Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs
R. Fu, Q. Hu, X. Dong, Y. Guo, Y. Gao, B. Li
British Machine Vision Conference (BMVC), 2020 (Oral, 30/670)
arXiv/ Code

We introduce two axioms -- Conservation and Sensitivity -- to the visualization paradigm of the CAM methods. Meanwhile, a dedicated Axiom-based Grad-CAM (XGrad-CAM) is proposed to satisfy these axioms as much as possible. Experiments demonstrate that XGrad-CAM is able to achieve better visualization performance than Grad-CAM, while also be class-discriminative and easy-to-implement compared with Grad-CAM++ and Ablation-CAM.

PontTuset

CGAN-Net: Class-Guided Asymmetric Non-local Network for Real-time Semantic Segmentation
H. Chen, Q. Hu, J. Yang, J. Wu, Y. Guo
International Conference on Acoustics, Speech, & Signal Processing (ICASSP), 2021

we introduce a Class-Guided Asymmetric Non-local Network (CGAN-Net) to enhance the class-discriminability in learned feature map, while maintaining real-time efficiency. The key to our approach is to calculate the dense similarity matrix in coarse semantic prediction maps, instead of the high-dimensional latent feature map.

PontTuset

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.

PontTuset

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)
Demo

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.

PontTuset

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.

PontTuset

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.

PontTuset

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.

PontTuset

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.

Teaching

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.