Demo Data from EDBT 2026 / reviewers in the wild / expert
Taiga Nishiyama
dblp:205/5908
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 first-author
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 · 50% Robot navigation and mapping · 50% | |
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 77% Geometric modeling and processing · 23% |
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 › 3d reconstruction
reconstruction |
0.4 | 1 | 2020 | AirPlanes: a DIY modeling system using mobile VSLAM for indoor space · MobiSys 2020 |
Robotics › Robot navigation and mapping › SLAM
visual SLAM |
0.4 | 1 | 2020 | AirPlanes: a DIY modeling system using mobile VSLAM for indoor space · MobiSys 2020 |
Virtual and augmented reality
augmented reality |
0.4 | 1 | 2020 | AirPlanes: a DIY modeling system using mobile VSLAM for indoor space · MobiSys 2020 |
Geometric modeling and processing › shape modeling
3d modeling |
0.1 | 1 | 2020 | AirPlanes: a DIY modeling system using mobile VSLAM for indoor space · MobiSys 2020 |
Methods — techniques the papers use, named apart from their topics
visual SLAM · 0.9ARKit · 0.9ARCore · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | AirPlanes: a DIY modeling system using mobile VSLAM for indoor spaceabstractThere is growing interest in using augmented reality technology for gaming, navigation, and remote communication. Although 3D space models can be made manually or digitalized using specialized and expensive sensing devices like LiDAR (Light Detection and Ranging), which are costly and time-consuming, the recently developed ARCore for Android and ARkit for iOS are convenient and quick means of developing AR applications. Their recognition performance is poor for flat and monotone walls. This drawback is significant because most walls in indoor environments are flat and only a small portion of them are colorful enough to detect feature points. Taiga Nishiyama, Daichi Yoshikawa, Nobuhiko Nishio, Kota Tsubouchi |
MobiSys | 1 |