Demo Data from EDBT 2026 / reviewers in the wild / expert
Tobias Güdelhöfer
dblp:325/3058
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
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.
| Human-computer interaction and pervasive computing
1 paper |
Immersive interaction · 100% | |
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › point cloud
point cloud annotation |
0.2 | 1 | 2023 | PointCloudLab: An Environment for 3D Point Cloud Annotation with Adapted Visual Aids and Levels of Immersion · ICRA 2023 |
Methods — techniques the papers use, named apart from their topics
controlled experiment · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | PointCloudLab: An Environment for 3D Point Cloud Annotation with Adapted Visual Aids and Levels of ImmersionabstractThe annotation of 3D point cloud datasets is an expensive and tedious task. To optimize the annotation process, recent works have proposed the use of environments with higher levels of immersion in combination with different types of visual aids. However, two problems remain unresolved. First, the proposed environments limit the user to a unique level of immersion and a fixed hardware setup. Second, their design overlooks the interaction effects between the level of immersion and the visual aids on the quality of the annotation process. To address these issues, we propose PointCloudLab, an environment for 3D point cloud annotation that allows the use of different levels of immersion that work in combination with visual aids. Using PointCloudLab, we conducted a controlled experiment (N=20) to investigate the effects of levels of immersion and visual aids on the annotation process. Our findings reveal that higher levels of immersion combined with object-based visual aids lead to a faster and more accurate annotation. Furthermore, we found significant interaction effects between the levels of immersion and the visual aids on the accuracy of the annotation. Achref Doula, Tobias Güdelhöfer, Andrii Matviienko, Max Mühlhäuser, Alejandro Sánchez Guinea |
ICRA | 2 |