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Welcome to the homepage of Yihong WU ÖÐÎÄ°æ |
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¡¡ Professor ¡¡ National Laboratory of Pattern Recognition Chinese Academy of Sciences P.O. Box 2728, Beijing, 100080 P.R. China ¡¡ Email: yhwu at nlpr.ia.ac.cn Tel: +86-10-82544697 Fax: +86-10-82544594 |
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3DV 2020 Tutorial: Visual Localization in the Age of Deep Learning ACCV 2018 Tutorial: Developments of 3 Dimensional Computer Vision Since 2017 PDF ____________________________________________________________________________________________ ¡¡ Research Interests 1).Camera calibration and
pose determination, image matching, three-dimensional reconstruction, vision
geometry, SLAM, vision on mobile devices. 2).Geometric invariants
and applications in computer vision and pattern recognition. 3).Polynomial elimination and applications in
computer vision. 4). Robot navigation, AR, VR. ¡¡ ¡¡ Selected Publications 1. Chenhui
Shi, Fulin Tang, Ning An, Yihong Wu. A Super Lightweight Neural
Representation for Large-scale 3D Mapping, CVPR, 2025. 2. Chade Li, Pengju Zhang, Bo Liu, Hao
Wei, Yihong Wu. FEAST-Mamba: FEAture and SpaTial Aware Mamba
Network with Bidirectional Orthogonal Fusion for Cross-Modal Point Cloud
Segmentation. AAAI, 2025. 3. Yidi Zhang, Fulin Tang, Yihong
Wu. CornerVINS: Accurate Localization and Layout Mapping for Structural
Environments Leveraging Hierarchical Geometric Representations, IEEE
Transactions on Robotics, 2025. 4. Zewen Xu, Yija He, Hao Wei, Yihong
Wu. DOGE: An Extrinsic Orientation and Gyroscope Bias Estimation for
Visual-Inertial Odometry Initialization, ICRA, 2025. 5. Chenhui Shi, Fulin Tang, Yihong
Wu, Hongtu Ji, Hongjie Duan. Accurate and Complete Neural Implicit
Surface Reconstruction in Street Scenes Using Images and LiDAR Point Clouds,
ISPRS Journal of Photogrammetry and Remote Sensing, 2025. 6. Yidi Zhang, Ning An, Chenhui
Shi, Shuo Wang, Hao Wei, Pengju Zhang, Xinrui Meng, Zengpeng Sun, Jinke
Wang, Wenliang Liang, Fulin Tang, Yihong Wu. CID-SIMS: Complex
Indoor Dataset with Semantic Information and Multi-sensor Data from a Ground
Wheeled Robot Viewpoint. The International Journal of Robotics Research, 43(7):899-917,
2024. 7. Bingxi Liu, Yujie Fu, Feng Lu,
Jinqiang Cui, Yihong Wu, and Hong Zhang. NPR: Nocturnal Place
Recognition Using Nighttime Translation in Large-Scale Training Procedures.
IEEE Journal of Selected Topics in Signal Processing, 18(3): 368-379, 2024. 8. Haolin Wang, Hao Wei, Zewen Xu,
Zeren Lv, Pengju Zhang, Ning An, Fulin Tang, Yihong Wu. RSS:
Robust Stereo SLAM With Novel Extraction and Full Exploitation of Plane
Features. IEEE Robotics and Automation Letters, 9(6): 5158-5165, 2024. 9. Yujie Fu, Pengju Zhang, Fulin
Tang, Yihong Wu, Covariant Peak Constraint for Accurate Keypoint
Detection and Keypoint-Specific Descriptor. IEEE Transactions on
Multimedia, 26(15): 5383-5397, 2024. 10. Yujie Fu, Pengju Zhang, Bingxi
Liu, Zheng Rong, Yihong Wu. Learning to Reduce Scale
Differences for Large-Scale Invariant Image Matching. IEEE Transactions
on Circuits and Systems for Video Technology, 33(3):1335-1348, March,
2023. 11. Chenhui Shi, Fulin Tang, Yihong
Wu, Xin Jin, Gang Ma. Accurate Implicit Neural Mapping With More Compact
Representation in Large-Scale Scenes Using Ranging Data. IEEE Robotics and
Automation Letters, 8(10): 6683-6690, 2023. 12. Shiyi Guo, Fulin Tang, Bingxi
Liu, Yujie Fu, Yihong Wu. An Accurate Outlier Rejection Network
With Higher Generalization Ability for Point Cloud Registration.IEEE Robotics
and Automation Letters£¬8(8):
4649-4656, 2023. 13. Zewen Xu. Zewen Xu, Hao Wei,
Fulin Tang, Yidi Zhang, Yihong Wu. PLPL-VIO: A Novel
Probabilistic Line Measurement Model for Point-Line-based Visual-Inertial
Odometry. IROS 2023. 14. Fulin Tang, Shaohuai Wu,
Zhengda Qian, Yihong Wu. Efficient 6D Camera Pose Tracking with
Circular Edges. Computer Vision and Image Understanding, 235:103767, 1-9,
2023. 15. Pengju Zhang, Bingxi
Liu, Yihong Wu. Leveraging Local and Global Descriptors in
Parallel to Search Correspondences for Visual Localization. Pattern
Recognition, Vol. 122, 2022. 16. Shiyi Guo, Zheng Rong, Yihong
Wu. Keyframe-based LiDAR SLAM with Robust Feature Extraction and Efficient
Mapping, to IEEE Transactions on Instrumentation and Measurement£¬71:
8501711£¬2022. 17. Hao Wei£¬Fulin
Tang, Zewen Xu, Yihong Wu. Structural Regularity Aided
Visual-Inertial Odometry With Novel Coordinate Alignment and Line
Triangulation. IEEE Robotics and Automation Letters£¬ 7(4):
10613-10620, 2022. 18. Hao Wei, Fulin Tang, Zewen Xu,
Chaofan Zhang, Yihong Wu. A Point-Line VIO System with Novel
Feature Hybrids and with Novel Line Predicting-Matching. IEEE Robotics
and Automation Letters, 6(4): 8681-8688, 2021. 19. Xiaomei Zhao, Yihong
Wu, Guidong Song, Zhenye Li, Yazhuo Zhang,
Yong Fan. A Deep Learning Model Integrating FCNNs and CRFs for Brain
Tumor Segmentation. Medical Image Analysis, Vol. 43, pp. 98-111, 2018. 20. Zhijun Dai, Yihong
Wu, Fengjun Zhang, Hongan Wang. A Novel Fast Method
for L_infinity Problems in Multiview Geometry. ECCV,
pp. 116-129, 2012. 21. Youji Feng, Lixin Fan, Yihong
Wu. Fast Localization in Large Scale Environments Using Supervised
Indexing of Binary Features. IEEE Transactions on Image Processing, Vol.
25, No. 1, pp. 343-358, 2016. 22. Yihong Wu, Zhanyi Hu,
Youfu Li. Radial Distortion Invariants and Lens Evaluation under a
Single-Optical-Axis Omnidirectional Camera. Computer Vision
and Image Understanding, 126: 11-27, 2014. 23. Yihong Wu, Youfu.
Li, Zhanyi Hu. Detecting and Handling Unreliable Points for Camera Parameter
Estimation. International Journal of Computer Vision (IJCV), Vol. 79,
No. 2, pp. 209-223, 2008. 24. Pierre Gurdjos, Peter
Sturm, Yihong Wu. Euclidean Structure from N>=2 Parallel
Circles: Theory and Algorithms. The 10th European Conference on Computer
Vision (ECCV), pp. 238-252, 2006. 25. Yihong Wu, Xinju Li, Fuchao
Wu, Zhanyi Hu. Coplanar Circles, Quasi-Affine Invariance and Calibration.
Image and Vision Computing, Vol. 24, Iss. 4, pp. 319-326, 2006. 26. Yihong Wu and
Zhanyi Hu. PnP Problem Revisited. Journal of Mathematical Imaging and Vision,
Vol. 24, No. 1, pp. 131-141, 2006. 27. Yihong Wu and
Zhanyi Hu. Geometric Invariants and Applications
under Catadioptric Camera Model. The 10th International Conference
on Computer Vision (ICCV), pp.1547-1554, 2005. 28. Yihong Wu, Haijiang
Zhu, Zhanyi Hu, Fuchao Wu. Camera Calibration from the Quasi-Affine
Invariance of Two Parallel Circles. The 8th European Conference on Computer
Vision (ECCV), pp. 190-202, 2004. 29. Yihong Wu and
Zhanyi Hu. Invariant Representations of a Quadric Cone and a Twisted Cubic.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),
Vol. 25, No. 10, pp. 1329-1332, 2003. 30. Hongbo Li and Yihong
Wu. Automated Short Proof Generation with Cayley and Bracket
Algebras I. Incidence Geometry. Journal of Symbolic Computation, Vol.
36, Iss. 5, pp. 717-762, 2003. 31. Hongbo Li and Yihong
Wu. Automated Short Proof Generation with Cayley and Bracket
Algebras II. Conic Geometry. Journal of Symbolic Computation, Vol.
36, Iss. 5, pp. 763-809, 2003. 32. Huishi Li and Yihong
Wu. Filtered-Graded Transfer of Groebner Basis Computation in
Solvable Polynomial Algebra. Communications in Algebra, Vol.28, No.1, pp.
15-32, 2000. ¡¡¡¡ Experience
¡¡ Teaching Experience
Spring 2017-now, Robot
navigation,
University of Chinese
Academy of Sciences Activities Area Chairs of CVPR2021¡¢CVPR2023¡¢NeurIPS2023¡¢ NeurIPS2023¡¢ICLR2024¡¢ICLR2025 Associate Editor of Pattern Recognition Area Chair of ICPR 2018 Area Chair of PRCV 2021 SPC of IJCAI 2019/2020/2021 Editorial Board Member of ACTA AUTOMATICA SINICA Editorial Board Member of Journal of CAD & CG Editorial Board Member of Journal of Frontiers of Computer Science and Technology Editorial Board Member of the Open Artificial Intelligence /Computer Science Journal PC Member of VISUAL 2007 PC Member of ICCV 2007 PC Member of PCM 2007 PC Member of WCICA 2008 PC Member of CVPR 2008 Session Chair of ACCV 2007 Session Chair of RIUPEEEC 2005 PC Member of KES 2009 Reviewer of ICCV 2009/ACCV 2009 ¡¡ ¡¡ Research Projects as PI Single view based metrology (863) A study on the theory and algorithm of camera self-calibration Geometric invariant computation and camera pose determination
from n perspective points Image based 3-dimensional reconstruction Camera parameter computation from video sequence (Key NSFC) Ominidirectional camera calibration Study on image invariants and applications under multiple camera models (NSFC) Image-based modeling for complex and large scale environment (NLPR) Visual SLAM (Nokia RC in Finland) Non-planar object tracking (Samsung) Camera pose tracking (Samsung) VR pose tracking (Huawei) ¡¡ ¡¡ The First Workshop on Community Based 3D Content and Its Applications in Mobile Internet Environments,In conjunction with ACCV 2009 The Second Workshop on Community Based 3D Content and Its
Applications,In conjunction with ICME 2012 Developments of 3 Dimensional Computer Vision
Since 2017 Virtual
reality (VR), augmented reality (AR), robotics, and autonomous driving have recently attracted much attention from the
academic as well as the industrial community. 3 dimensional (3D) computer
vision plays important roles in these fields. Autonomous localization and
navigation is necessary for a moving robot, where using cameras is the most
flexible and low cost approach for building map and localization. To augment
reality in images, camera pose determination or localization is needed. To
view virtual environments, the corresponding viewing angle is necessary to be
computed. Furthermore, cameras are ubiquitous and people carry mobile phones
that have cameras every day. There have been some AR applications in mobile
phones. Therefore, 3D computer vision has great and widespread applications. This
tutorial will provide the developments of 3D computer vision of the past two
years. The important works since 2017 will be introduced in image matching,
camera localization including camera pose determination and simultaneous
localization and mapping (SLAM), 3D reconstruction. The
contents have five parts: 1.
Preface Some
fundamental knowledge of 3D vision is introduced as well as some events
related to 3D vision in this past two years. 2.
Developments in image matching Some
important works of image feature detectors and descriptors since 2017 are
introduced. Also, some important woks of image matching and two dataset since
2017 are introduced. Among these works, deep learning based methods are
growing. 3.
Developments in camera localization A
complete classification of image based camera localization mapped as a tree
structure is given. The important directions are pointed out. The
developments of camera localization in both known environments and unknown
environments are introduced since 2017. The ones in known environments are
PnP works. The ones in unknown environments are SLAM works. SLAM works
include the general geometric SLAM, learning SLAM, semantic SLAM, and marker
SLAM. Except SLAM under the traditional cameras, there are some SLAM works
under the event cameras and RGBD cameras. 4.
Developments in 3D reconstruction This
part will introduce structure from motion (SFM) based 3D reconstruction,
learning 3D reconstruction, RGBD 3D reconstruction or RGBD SLAM since
2017. 5.
Trends of 3D vision I will share my views for 3D vision
trends. 3DV 2020 Tutorial: Visual Localization in the Age of Deep Learning
A short description: Deep learning is a subset of machine learning
and one of the foundations of Artificial Intelligence. Recent years
see its remarkable development and wide applications. It has been used in
almost every task of computer vision field and shows its powerful ability. 3D
computer vision, developed under the Marr framework, re-blooms by deep
learning again. Visual localization, a key task in 3D computer vision, has
many applications in AR, VR, robot navigation, and driverless car. Deep
learning has been used in visual localization by various ways. There are
end-to-end and non-end-to-end deep learning visual localizations. The main
topics in visual localization include image feature extraction and
description, image matching, image retrieval, 2D-3D matching, RANSAC, PnP,
feature tracking, loop closure detection, SLAM, bundle adjustment. All these
have combined deep learning. The
tutorial consists of five parts. In the first part, this tutorial will give
an overview of visual localization, share with some personal viewpoints to
use deep learning in 3D vision. In the second part, the tutorial will
introduce some important literatures in image feature extraction and
description, and matching with deep learning. Then, work of camera pose
determinations from known 3D structure knowledge with deep learning is
reported in the third part, including the work of image retrieval, 2D-3D
matching, RANSAC, and PnP. In the fourth part, work of SLAM with deep
learning is reported, including the work of feature tracking, loop closure
detection, bundle adjustment etc. As we see, every aspect combines deep
learning in visual localization. It shows free exploration to use deep
learning. Finally, i.e. in the last part, some personal views are shared for
some existing problems when using deep learning, for example, there are no
clear training dataset and test dataset given in some public dataset.
Besides, future trends for visual localization are also shared. Syllabus: The
contents have five parts: 1.
An overview of visual localization Some
fundamental knowledge of 3D vision and an overview of image-based
localization are introduced. A complete classification of image-based
localization mapped as a tree structure is given. The possible best way to
use deep learning in 3D vision is also analyzed. 2.
Image feature representation and matching with deep learning Some
important literatures of image feature detectors and descriptors with deep
learning are introduced. Also, some important literatures of image matching
with deep learning and dataset are introduced. 3.
Camera pose determination from known 3D structure knowledge with deep
learning Deserved
studies with deep learning are pointed out. The developments of camera
localization in known environments are introduced. PnP and RANSAC using deep
learning are also presented. 4.
SLAM with deep learning This
part will introduce some learning-based SLAM, recent new dataset, and
challenges. It also includes feature tracking from videos, loop closure
detection, bundle adjustment etc. 5.
Problems and trends when view geometry using deep learning Some
personal views are shared for some problems and trends when view geometry
using deep learning. _____________________________________________________________________________________________
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