Demo Data from EDBT 2026 / reviewers in the wild / expert
Lavrenti Frobeen
dblp:211/7246
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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.
| Computer graphics and multimedia
1 paper |
Rendering · 91% Computational photography and imaging · 9% | |
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › 3d face reconstruction
3d morphable model fitting |
0.3 | 1 | 2017 | Efficient Global Illumination for Morphable Models · ICCV 2017 |
Computer vision › 3D vision
3d reconstruction |
0.3 | 1 | 2017 | Efficient Global Illumination for Morphable Models · ICCV 2017 |
Rendering
global illumination |
0.3 | 1 | 2017 | Efficient Global Illumination for Morphable Models · ICCV 2017 |
Rendering › global illumination
precomputed radiance transfer |
0.3 | 1 | 2017 | Efficient Global Illumination for Morphable Models · ICCV 2017 |
Rendering › shadow rendering
self-shadowing |
0.3 | 1 | 2017 | Efficient Global Illumination for Morphable Models · ICCV 2017 |
Computational photography and imaging
illumination modeling |
0.1 | 1 | 2017 | Efficient Global Illumination for Morphable Models · ICCV 2017 |
Methods — techniques the papers use, named apart from their topics
spherical harmonics · 0.6ray casting · 0.6linear model · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Efficient Global Illumination for Morphable ModelsabstractWe propose an efficient self-shadowing illumination model for Morphable Models. Simulating self-shadowing with ray casting is computationally expensive which makes them impractical in Analysis-by-Synthesis methods for object reconstruction from single images. Therefore, we propose to learn self-shadowing for Morphable Model parameters directly with a linear model. Radiance transfer functions are a powerful way to represent self-shadowing used within the precomputed radiance transfer framework (PRT). We build on PRT to render deforming objects with self-shadowing at interactive frame rates. It can be illuminated efficiently by environment maps represented with spherical harmonics. The result is an efficient global illumination method for Morphable Models, exploiting an approximated radiance transfer. We apply the method to fitting Morphable Model parameters to a single image of a face and demonstrate that considering self-shadowing improves shape reconstruction. Sandro Schönborn, Bernhard Egger 0001, Lavrenti Frobeen, Thomas Vetter |
ICCV | 4 |