Paper on RGB-D SLAM of Dr. ZHANG Yu's group accepted by TPAMI

"RGB-D SLAM in Dynamic Environments Using Point Correlations" (DSLAM), a paper of Dr. ZHANG Yu’s group has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). The work focuses on the simultaneous localization and mapping (SLAM) in dynamic environment. It is the first paper accepted by TPAMI whose first author affiliation is the College of Control Science and Engineering, Zhejiang University. Moreover, it is also the first SLAM paper accepted by TPAMI whose first author affiliation is domestic university or research institution.In this paper, a simultaneous localization and mapping (SLAM) method that eliminates the influence of moving objects in dynamic environments is proposed. This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups. A sparse graph is first created from all map points. In this graph, the vertices represent map points, and each edge represents the correlation between adjacent points. If the relative position between two points remains consistent over time, there is correlation between them, and they are considered to be moving together rigidly. If not, they are considered to have no correlation and to be in separate groups. After the edges between the uncorrelated points are removed, the remaining graph separates the map points of moving objects from the static scene. The largest group is assumed to be the group of reliable static map points. Experimental results demonstrate that robust and accurate performance can be achieved by the proposed SLAM method in both slightly and highly dynamic environments. Compared to other state-of-the-art methods, the proposed method can provide competitive accuracy with good real-time performance.To read the paper:https://ieeexplore.ieee.org/document/9145704Reporter: YU HongxiangEditor: WANG Jing

DMT clinches championship at 2020 iQIYI iCartoonFace Challenge

Under the umbrella of the College of Control Science and Engineering, a partnership between the the College and Alibaba DAMO Academy, a team of Zhejiang University students participated in and won the championship at the 2020 iQIYI iCartoonFace Challenge, an integral part of IJCAI PRICAI 2020.The team representing Zhejiang University was comprised of three students: HE Shuting (BA22), ZHANG Miao (BA21), and ZENG Zhaoyang (BA22). They came in first place in the iCartoonFace Recognition Challenge. This year iQIYI iCartoonFace Challenge attracted about 500 teams in China, such as Shanghai Jiao Tong University, the Chinese Academy of Sciences, the University of Science and Technology of China, Tencent and Dahua Technology.  The DMT team is under the guidance of Dr. LUO Hao, Prof. JIANG Wei and Prof. LIU Xinggao, as well as researchers from Alibaba DAMO Academy. It has been actively engaging in several competitions in the field of computer vision and achieved excellent records.  In the AI City Challenge 2020，DMT team innovatively integrated multi-domain learning and identity mining into vehicle city-Scale multi-Camera re-identification questions and was the third-place winner in the leader board of this track.This January the DMT team also participated in the 1st National Artificial Intelligence Challenge, co-hosted by Shenzhen Municipal Government and Shenzhen Tencent Information Technology Co., Ltd and won the third place in person re-identification track with a handsome sum of prize money (500,000 yuan).Reprinted from:  ZJU Newsroom

ZJU climbs to 53 in QS World University Rankings 2021

ZJU4H’s Handbook of FAQs about COVID-19 Prevention and Treatment Released

Prof. CHEN Jiming invited as member of IEEE VTS Fellow Evaluation Committee

Professor CHEN Jiming recently became a member of IEEE Vehicular Technology Society (VTS) Fellow Evaluation Committee, invited by the Committee Chair, Professor Gerhard Bauch at the Hamburg University of Technology.CHEN Jiming is a Changjiang Scholars Professor with College of Control Science and Engineering, Zhejiang University. He is vice Dean of Facutly of Information Technology,  Deputy Director of the State Key laboratory of Industrial Control Technology, Director of Industrial Process Control. He was a visiting researcher at University of Waterloo from 2008 to 2010. Currently, He serves/served associate editors for ACM TECS, IEEE TPDS, IEEE Network, IEEE TCNS, IEEE TII, etc. He has been appointed as a distinguished lecturer of IEEE vehicular technology society 2015, and selected in National Program for Special Support of Top-Notch Young Professionals, and also funded Excellent Youth Foundation of NSFC. He also was the recipients of IEEE INFOCOME 2014 Best Demo Award, IEEE ICCC 2014 best paper award, IEEE PIMRC 2012 best paper award, and JSPS Visiting Fellowship 2011. He also received the IEEE Comsoc Asia-pacific Outstanding Young Researcher Award 2011. He is a Distinguished Lecturer of IEEE Vehicular Technology Society (2015-2018), and a Felllow of IEEE. His research interests include networked control, sensor networks, cyber security, IoT.Reporter: REN TongEditor: WANG Jing

ZJU now 13th in THE Asia University Rankings

Zhejiang University celebrates 123rd anniversary

Paper of Prof. CHENG Peng's group accepted by SIGCOMM 2020

"VTrace: Automatic Diagnostic System for Persistent Packet Loss in Cloud-Scale Overlay Network",  a paper on cloud network fault location of Professor CHENG Peng and CHEN Jiming’s group has been accepted by SIGCOMM 2020. It is the first paper accepted by SIGCOMM main conference whose first author affiliation is Zhejiang University, and it is also the first accepted paper on cloud network from mainland China.The work is highly supported by Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies (AZFT). FANG Chongrong and LIU Haoyu, doctoral students at Zhejiang University, completed VTrace through the long-term close cooperation with Alibaba Cloud.Abstract:Persistent packet loss in the cloud-scale overlay network severely compromises tenant experiences. Cloud providers are keen to automatically and quickly determine the root cause of such problems. However, existing work is either designed for the physical network or insufficient to present the concrete reason for packet loss. In this paper, we propose to record and analyze the on-site forwarding condition of packets during packet-level tracing. The cloud-scale overlay network presents great challenges to achieve this goal with its high network complexity, multi-tenant nature, and the diversity of root causes. To address these challenges, we present VTrace, an automatic diagnostic system for persistent packet loss over the cloud-scale overlay network. Utilizing the "fast path-slow path" structure of virtual forwarding devices (VFDs), e.g., vSwitches, VTrace installs several "coloring, matching and logging" rules in VFDs to selectively track the packets of interest and inspect them in depth. The detailed forwarding situation at each hop is logged and then assembled to perform analysis with an efficient path reconstruction scheme. Experiments are conducted to demonstrate VTrace’s low overhead and quick responsiveness. We share the experiences of how VTrace efficiently resolves persistent packet loss issues after deploying it in Alibaba Cloud for over 20 months.Reporter: REN TongEditor: WANG Jing

A tour to ZJUMAA on International Museum Day 2020

ZJU students celebrate graduation despite ceremony cancellation