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Welcome to the homepage of Yihong WU 中文版 |
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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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**中国科学院 自动化研究所 模式识别国家重点实验室** **机器人视觉课题组** 主要研究方向： ----------------------------------------------------------------------------------------------------------- 3DV 2020 Tutorial: Visual Localization in the Age of Deep Learning
ACCV 2018 Tutorial: Developments of 3 Dimensional Computer Vision Since 2017 PDF _____________________________________________________________________________________________ Research Demos (Pose
tracking): 1. Vision-based mobile augmented reality
2. SLAM for a general moving object
3. SLAM for scenes
4. SLAM relocalization
5. SLAM with IMU
6. SLAM devices Realtime and online, outdoor and indoor 
Research Interests 1. Camera
calibration and pose determination, image matching, three-dimensional
reconstruction, vision geometry, SLAM, vision on mobile devices. 2. 3. Polynomial elimination and applications in computer vision.
Selected Publications
Work Experience
Education
Teaching Experience
Spring 2017-now, Robot navigation, University of Chinese Academy of Sciences Activities Area Chair of CVPR 2021 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.
Visual Localization in the Age of Deep Learning Time: JST 10:00----13:00 Nov. 28 CST 09:00----12:00 Nov. 28 GMT 01:00----04:00 Nov. 28 EST 20:00----23:00 Nov. 27 PST 17:00----20:00 Nov. 27 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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