Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jozef Dobos
dblp:54/9988
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorArtificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 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 · 72% Trustworthy machine learning · 28% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › uncertainty estimation
confidence estimation |
0.1 | 1 | 2011 | Capturing Time-of-Flight data with confidence · CVPR 2011 |
Computer vision › 3D vision › range sensing
depth sensing |
0.1 | 1 | 2011 | Capturing Time-of-Flight data with confidence · CVPR 2011 |
Computer vision › 3D vision › depth estimation
time-of-flight depth |
0.1 | 1 | 2011 | Capturing Time-of-Flight data with confidence · CVPR 2011 |
Computer vision › 3D vision
3d reconstruction |
0.0 | 1 | 2011 | Capturing Time-of-Flight data with confidence · CVPR 2011 |
Computer vision › 3D vision
point cloud processing |
0.0 | 1 | 2011 | Capturing Time-of-Flight data with confidence · CVPR 2011 |
Methods — techniques the papers use, named apart from their topics
random forest regressor · 0.1per-pixel confidence measure · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | 3D Timeline: Reverse engineering of a part-based provenance from consecutive 3D modelsabstractAbstract We present a novel tool for reverse engineering of modeling histories from consecutive 3D files based on a timeline abstraction. Although a timeline interface is commonly used in 3D modeling packages for animations, it has not been used on geometry manipulation before. Unlike previous visualization methods that require instrumentation of editing software, our approach does not rely on pre‐recorded editing instructions. Instead, each stand‐alone 3D file is treated as a keyframe of a construction flow from which the editing provenance is reverse engineered. We evaluate this tool on six complex 3D sequences created in a variety of modeling tools by different professional artists and conclude that it provides useful means of visualizing and understanding the editing history. A comparative user study suggests the tool is well suited for this purpose. Jozef Dobos, Niloy J. Mitra, Anthony Steed |
Comput. Graph. Forum | 1 |
| 2013 | On the existence of equitable cake divisions
Katarína Cechlárová, Jozef Dobos, Eva Pillárová |
Inf. Sci. | 2 |
| 2011 | Capturing Time-of-Flight data with confidenceabstractTime-of-Flight cameras provide high-frame-rate depth measurements within a limited range of distances. These readings can be extremely noisy and display unique errors, for instance, where scenes contain depth discontinuities or materials with low infrared reflectivity. Previous works have treated the amplitude of each Time-of-Flight sample as a measure of confidence. In this paper, we demonstrate the shortcomings of this common lone heuristic, and propose an improved per-pixel confidence measure using a Random Forest regressor trained with real-world data. Using an industrial laser scanner for ground truth acquisition, we evaluate our technique on data from two different Time-of-Flight cameras1. We argue that an improved confidence measure leads to superior reconstructions in subsequent steps of traditional scan processing pipelines. At the same time, data with confidence reduces the need for point cloud smoothing and median filtering. Malcolm Reynolds, Jozef Dobos, Leto Peel, Tim Weyrich, Gabriel J. Brostow |
CVPR | 2 |