Demo Data from SIGIR 2026 / reviewers in the wild / expert
Andrew Starostin
dblp:225/8430
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Ubiquitous computing and smart environments › location sensing
location tracking |
0.3 | 1 | 2018 | CarpetVR: The Magic Carpet Meets the Magic Mirror · VR 2018 |
Immersive interaction › virtual reality
mobile virtual reality |
0.3 | 1 | 2018 | CarpetVR: The Magic Carpet Meets the Magic Mirror · VR 2018 |
Virtual and augmented reality › tracking
marker-based tracking |
0.1 | 1 | 2018 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | CarpetVR: The Magic Carpet Meets the Magic MirrorabstractWe 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 |
VR | 3 |