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Taiga Nishiyama

dblp:205/5908 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › 3d reconstruction
reconstruction
0.412020
AirPlanes: a DIY modeling system using mobile VSLAM for indoor space · MobiSys 2020
Robotics › Robot navigation and mapping › SLAM
visual SLAM
0.412020
AirPlanes: a DIY modeling system using mobile VSLAM for indoor space · MobiSys 2020
Virtual and augmented reality
augmented reality
0.412020
AirPlanes: a DIY modeling system using mobile VSLAM for indoor space · MobiSys 2020
Geometric modeling and processing › shape modeling
3d modeling
0.112020
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
YearPublicationVenuePosition
2020 AirPlanes: a DIY modeling system using mobile VSLAM for indoor space
abstract
There 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
MobiSys1