Demo Data from EDBT 2026 / reviewers in the wild / expert
Hailong Qian
dblp:154/2541
· DBLP profile ↗
4ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Geometric modeling and processing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing
mesh deformation |
0.2 | 1 | 2016 | Robust mesh deformation with salient features preservation · Sci. China Inf. Sci. 2016 |
Geometric modeling and processing › mesh processing
feature preservation |
0.1 | 1 | 2016 | Robust mesh deformation with salient features preservation · Sci. China Inf. Sci. 2016 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | A UAV tracking framework via motion-decoupled trajectory prediction and occlusion handling
Tianxing Xiao, Yonghua Lu, Xinyu Di, Zhicheng Ma, Hailong Qian, Yinlong Zhu |
Inf. Sci. | 5 |
| 2020 | Trajectory Planning for Unmanned Aircraft Vehicle via Set-Valued FilterabstractTrajectory planning in complex environments with different kinds of obstacles, like static obstacles, dynamic obstacles and noncooperative agents, is of great pratical importance for Unmanned Aircraft Vehicle (UAV). Although various algorithms are proposed to solve the obstacle avoidance problem, these methods only consider one or two kinds of obstacles. In this paper, a novel algorithm is proposed to plan a trajectory for UAV in such an environment which simultaneously includes static obstacles, dynamic obstacles and especially noncooperative agents. Besides, we consider different types of approach modes of noncooperative agents and propose corresponding strategies to avoid collisions with them. A set-valued filter-based method is proposed to predict the position of dynamic obstacles and noncooperative agents whose motion model is not available to UAV. Meanwhile, the measurement noise is considered in the set-valued filter to improve the safety of UAV. Some simulations are implemented to verify the effectiveness of the proposed method and they confirm that the proposed method can plan a safe trajectory for UAV in complex environments. Hailong Qian, Weimin Zhong, Chaoqiang Zhao, Wenle Zhang, Yang Tang 0001 |
IECON | 1 |
| 2016 | Robust mesh deformation with salient features preservation
Yong Zhao 0004, Shengjie Lu, Hailong Qian, Pengcheng Yao |
Sci. China Inf. Sci. | 3 |
| 2015 | A deformation-aware hierarchical framework for shape-preserving editing of static and time-varying mesh data
Yong Zhao 0004, Hailong Qian, Shengjie Lu |
Comput. Graph. | 2 |