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Andrew Starostin

dblp:225/8430 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1

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% Ubiquitous computing and smart environments · 50%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%

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

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments › location sensing
location tracking
0.312018
CarpetVR: The Magic Carpet Meets the Magic Mirror · VR 2018
Immersive interaction › virtual reality
mobile virtual reality
0.312018
CarpetVR: The Magic Carpet Meets the Magic Mirror · VR 2018
Virtual and augmented reality › tracking
marker-based tracking
0.112018
CarpetVR: The Magic Carpet Meets the Magic Mirror · VR 2018

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

sensor fusion · 0.7gyroscope · 0.7computer vision · 0.7accelerometer · 0.7
YearPublicationVenuePosition
2018 CarpetVR: The Magic Carpet Meets the Magic Mirror
abstract
We present CarpetVR - a new system for marker-based positional tracking suitable for mobile VR (Figure 1). The system utilizes all sensors present on a modern smartphone (a camera, a gyroscope, and an accelerometer) and does not require any additional sensors. CarpetVR uses a single floor marker that we call the magic carpet. CarpetVR augments a standard mobile VR setup with a slanted mirror that can be attached either to the smartphone or to the head mount in front of the smartphone camera. As the person walks over the marker, the smartphone camera is able to see the marker thanks to the reflection in the mirror. Our tracking engine then uses a computer vision module to detect the marker and to estimate the smartphone position with respect to the marker at 40 frames per second. This estimate is integrated with high framerate signals from the gyroscope and the accelerometer. The resulting estimates of the position and the orientation are then used to render the virtual world. Our sensor fusion algorithm ensures minimal-latency tracking with very little jitter.
Victor S. Lempitsky, Alexander Vakhitov, Andrew Starostin
VR3