George Xu

dblp:164/8206 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 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 · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › range sensing
depth sensing
0.212015
Sensitivity study for object reconstruction using a network of time-of-flight depth sensors · ICRA 2015
Computer vision › 3D vision › 3d reconstruction
object reconstruction
0.212015
Sensitivity study for object reconstruction using a network of time-of-flight depth sensors · ICRA 2015
Computer vision › 3D vision › camera calibration
depth calibration
0.112015
Sensitivity study for object reconstruction using a network of time-of-flight depth sensors · ICRA 2015

Methods — techniques the papers use, named apart from their topics

time-of-flight sensing · 0.2sensor calibration · 0.2
YearPublicationVenuePosition
2015 Sensitivity study for object reconstruction using a network of time-of-flight depth sensors
abstract
This paper investigates the integration of multiple time-of-flight (ToF) depth sensors for object reconstruction. The advantage of such a sensor network is in the increased viewing coverage and addressing the problem of object self-occlusion. However, in utilizing a network of depth sensors, calibration and interference between the sensors can be one of the key challenges of their effective utilization. In comparison with infrared depth sensors, integration of time-of-flight sensors offer some new challenges. This paper presents experimental studies of practical factors that can affect such integration and proposes a guideline which can be used to avoid interferences. Using the proposed set-up, the paper also presents a method which can be used for reconstructing objects in such a sensor network.
George Xu, Shahram Payandeh
ICRA1