David Mertens

dblp:11/9595 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 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 · 50% Haptics and multimodal interaction · 50%
Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Immersive interaction
mixed reality
1.012026
Aligning Realities: A Registration Pipeline for Arbitrary Objects in Mixed Reality Using Controller-Based Point Selection · VR 2026
Geometric modeling and processing
registration
0.312026
Aligning Realities: A Registration Pipeline for Arbitrary Objects in Mixed Reality Using Controller-Based Point Selection · VR 2026

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

point-to-point matching · 2.0motion capture · 2.0kabsch algorithm · 2.0
YearPublicationVenuePosition
2026 Aligning Realities: A Registration Pipeline for Arbitrary Objects in Mixed Reality Using Controller-Based Point Selection
abstract
A wide range of Mixed Reality (MR) applications rely on haptic interaction with real-world objects. These interactions range from grasping and standing on objects, to writing on their surfaces. Accurate alignment of a real-world object with its virtual counterpart enables users to perceive and interact with it both visually and haptically. However, existing registration methods often rely on tracked, external markers, are geometrically constrained, or require complex setups that limit their practical applicability. Hence, we present an integrated pipeline for rapid and accurate registration of arbitrary physical objects in MR. The proposed approach combines custom 3D-printed add-ons for Meta Quest 3 controllers with a point-to-point matching algorithm based on Kabsch optimization, enabling precise selection, calibration, and registration. The method is objectively evaluated using a modern motion capture rig against other state-of-the-art registration techniques. An additional user-centric evaluation helps to understand the general usability of our pipeline. Source code and datasets are released as open resources to encourage reproducibility and further research.
David Mertens, Steffen-Sascha Stein, Kristoffer Waldow, Arnulph Fuhrmann
VR1