Demo Data from EDBT 2026 / reviewers in the wild / expert
Jens Rannacher
dblp:83/9988
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—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.
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
motion estimation |
0.1 | 1 | 2011 | Feature- and depth-supported modified total variation optical flow for 3D motion field estimation in real scenes · CVPR 2011 |
Computer vision › 3D vision › motion estimation
optical flow |
0.1 | 1 | 2011 | Feature- and depth-supported modified total variation optical flow for 3D motion field estimation in real scenes · CVPR 2011 |
Computer vision › 3D vision › motion estimation › optical flow
variational optical flow |
0.1 | 1 | 2011 | Feature- and depth-supported modified total variation optical flow for 3D motion field estimation in real scenes · CVPR 2011 |
Computer vision › 3D vision › motion estimation
3d motion estimation |
0.0 | 1 | 2011 | Feature- and depth-supported modified total variation optical flow for 3D motion field estimation in real scenes · CVPR 2011 |
Computer vision › 3D vision
stereo vision |
0.0 | 1 | 2011 | Feature- and depth-supported modified total variation optical flow for 3D motion field estimation in real scenes · CVPR 2011 |
Methods — techniques the papers use, named apart from their topics
total variation · 0.1feature matching · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Feature- and depth-supported modified total variation optical flow for 3D motion field estimation in real scenesabstractWe propose and evaluate improvements in motion field estimation in order to cope with challenges in real world scenarios. To build a real-time stereo-based three-dimensional vision system which is able to handle illumination changes, textureless regions and fast moving objects observed by a moving platform, we introduce a new approach to support the variational optical flow computation scheme with stereo and feature information. The improved flow result is then used as input for a temporal integrated robust three-dimensional motion field estimation technique. We evaluate the results of our optical flow algorithm and the resulting three-dimensional motion field against approaches known from literature. Tests on both synthetic realistic and real stereo sequences show that our approach is superior to approaches known from literature with respect to density, accuracy and robustness. Thomas Müller 0008, Jens Rannacher, Clemens Rabe, Uwe Franke |
CVPR | 2 |