Demo Data from EDBT 2026 / reviewers in the wild / expert
Yedong Niu
dblp:168/8152
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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.
| Artificial intelligence
1 paper |
3D vision · 25% Robot navigation and mapping · 25% Autonomous driving · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
egocentric vision |
0.2 | 1 | 2016 | Egocentric Future Localization · CVPR 2016 |
Robotics › Robot navigation and mapping › localization
future localization |
0.2 | 1 | 2016 | Egocentric Future Localization · CVPR 2016 |
Robotics › Motion planning and robot control
motion planning |
0.2 | 1 | 2016 | Egocentric Future Localization · CVPR 2016 |
Robotics › Autonomous driving
trajectory prediction |
0.2 | 1 | 2016 | Egocentric Future Localization · CVPR 2016 |
Methods — techniques the papers use, named apart from their topics
egoretinal map · 0.2cost function minimization · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Egocentric Future LocalizationabstractWe presents a method for future localization: to predict plausible future trajectories of ego-motion in egocentric stereo images. Our paths avoid obstacles, move between objects, even turn around a corner into space behind objects. As a byproduct of the predicted trajectories, we discover the empty space occluded by foreground objects. One key innovation is the creation of an EgoRetinal map, akin to an illustrated tourist map, that 'rearranges' pixels taking into accounts depth information, the ground plane, and body motion direction, so that it allows motion planning and perception of objects on one image space. We learn to plan trajectories directly on this EgoRetinal map using first person experience of walking around in a variety of scenes. In a testing phase, given an novel scene, we find multiple hypotheses of future trajectories from the learned experience. We refine them by minimizing a cost function that describes compatibility between the obstacles in the EgoRetinal map and trajectories. We quantitatively evaluate our method to show predictive validity and apply to various real world daily activities including walking, shopping, and social interactions. Hyun Soo Park, Jyh-Jing Hwang, Yedong Niu, Jianbo Shi |
CVPR | 3 |