EDBT 2026 Demo / reviewers in the wild / expert
Robert Thiel
dblp:241/6384
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 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.
| Computer graphics and multimedia
1 paper |
Geometric modeling and processing · 50% Computational photography and imaging · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing › surface reconstruction
normal integration |
0.9 | 1 | 2025 | Discontinuity-Aware Normal Integration for Generic Central Camera Models · ICCV 2025 |
Methods — techniques the papers use, named apart from their topics
local planarity constraint · 0.9depth-normal relation modeling · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Discontinuity-Aware Normal Integration for Generic Central Camera ModelsabstractRecovering a 3D surface from its surface normal map, a problem known as normal integration, is a key component for photometric shape reconstruction techniques such as shape-from-shading and photometric stereo. The vast majority of existing approaches for normal integration handle only implicitly the presence of depth discontinuities and are limited to orthographic or ideal pinhole cameras. In this paper, we propose a novel formulation that allows modeling discontinuities explicitly and handling generic central cameras. Our key idea is based on a local planarity assumption, that we model through constraints between surface normals and ray directions. Compared to existing methods, our approach more accurately approximates the relation between depth and surface normals, achieves state-of-the-art results on the standard normal integration benchmark, and is the first to directly handle generic central camera models. Francesco Milano 0001, Manuel Lopez-Antequera, Naina Dhingra, Roland Siegwart, Robert Thiel |
ICCV | 5 |
| 2018 | NightOwls: A Pedestrians at Night Dataset
Lukás Neumann, Michelle Karg, Shanshan Zhang 0001, Christian Scharfenberger, Eric Piegert, Sarah Mistr, Olga Prokofyeva, Robert Thiel, Andrea Vedaldi, Andrew Zisserman, Bernt Schiele |
ACCV (1) | 8 |