Demo Data from EDBT 2026 / reviewers in the wild / expert
Kimmo Roimela
dblp:45/321
· DBLP profile ↗
7ranked-venue papers
2as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 6 · 2 first-authorHuman-computer interaction and ubiquitous computing · 2 · 1 first-authorArtificial intelligence and machine learning · 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
2 papers |
Virtual and augmented reality · 32% Multimedia systems and quality of experience · 32% Image and video coding · 24% | |
| Artificial intelligence
1 paper |
Robot navigation and mapping · 67% 3D vision · 33% | |
| Computer networks
1 paper |
Wireless sensing and localization · 100% |
Topics — the 8 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality
augmented reality |
0.2 | 1 | 2013 | Augmented and interactive video playback based on global camera pose · ACM Multimedia 2013 |
Robotics › Robot navigation and mapping
landmark detection |
0.1 | 1 | 2011 | City-scale landmark identification on mobile devices · CVPR 2011 |
Robotics › Robot navigation and mapping › localization
vision-based localization |
0.1 | 1 | 2011 | City-scale landmark identification on mobile devices · CVPR 2011 |
Computer vision › 3D vision
visual localization |
0.1 | 1 | 2011 | City-scale landmark identification on mobile devices · CVPR 2011 |
Image and video coding › texture compression
high dynamic range texture compression |
0.1 | 1 | 2006 | High dynamic range texture compression · ACM Trans. Graph. 2006 |
Image and video coding
texture compression |
0.1 | 1 | 2006 | High dynamic range texture compression · ACM Trans. Graph. 2006 |
Rendering
texture mapping |
0.1 | 1 | 2006 | High dynamic range texture compression · ACM Trans. Graph. 2006 |
Wireless sensing and localization › satellite navigation
GPS localization |
0.0 | 1 | 2011 | City-scale landmark identification on mobile devices · CVPR 2011 |
Methods — techniques the papers use, named apart from their topics
image retrieval · 0.2feature detection · 0.2benchmark construction · 0.2geo-metadata extraction · 0.2camera pose estimation · 0.2constant 8-bit encoding · 0.1DXTC · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Surface Lightfield Support in Video-based Point Cloud CodingabstractSurface light-field (SLF) is a mapping of a set of color vectors to a set of ray vectors that originate at a point on a surface. It enables rendering photo-realistic view points in extended reality applications. However, the amount of data required to represent SLF is significantly more. Therefore, storing and distributing SLFs requires an efficient compressed representation. The Motion Pictures Experts Group (MPEG) has an on-going standard activity for the compression of point clouds. Until recently, this activity was targeting compression of single texture information, but is now investigating view dependent textures. In this paper, we propose methods to optimize coding of view dependent color without compromising on the visual quality. Our results show the optimizations provided in this paper reduce coded HEVC bit rate by 64% for the all-intra configuration and 52% for the random-access configuration, when compared to coding all texture independently. Deepa Naik, Sebastian Schwarz, Vinod Kumar Malamal Vadakital, Kimmo Roimela |
MMSP | 4 |
| 2014 | A 3D map augmented photo gallery application on mobile deviceabstractThis paper proposes a 3D map augmented photo gallery mobile application that allows user to virtually transit from 2D image space to the 3D map space, to expand the field of view to surrounding environments that are not visible in the original image, and to change viewing angles among different global registered images during the image browsing. The processing of images consists of two main steps: in the first step, the client application uploads an image to the GeoImage Engine which extracts the geo-metadata and returns them back to the client; in the second step, the client application requests augmented content from server, and then renders 3D view of images on the screen of mobile devices. Junsheng Fu, Lixin Fan, Kimmo Roimela, Yu You, Ville-Veikko Mattila |
ICIP | 3 |
| 2013 | Augmented and interactive video playback based on global camera poseabstractThis paper proposes a video playback system that allows user to expend the field of view to surrounding environments that are not visible in the original video frame, arbitrarily change the viewing angles, and see the superimposed point-of-interest (POIs) data in an augmented reality manner during the video playback. The processing consists of two main steps: in the first step, client uploads a video to the GeoVideo Engine, and then the GeoVideo Engine extracts the geo-metadata and returns them back to the client; in the second step, client requests POIs from server, and then the client renders the video with POIs. Junsheng Fu, Lixin Fan, Yu You, Kimmo Roimela |
ACM Multimedia | 4 |
| 2011 | City-scale landmark identification on mobile devicesabstractWith recent advances in mobile computing, the demand for visual localization or landmark identification on mobile devices is gaining interest. We advance the state of the art in this area by fusing two popular representations of street-level image data - facade-aligned and viewpoint-aligned - and show that they contain complementary information that can be exploited to significantly improve the recall rates on the city scale. We also improve feature detection in low contrast parts of the street-level data, and discuss how to incorporate priors on a user's position (e.g. given by noisy GPS readings or network cells), which previous approaches often ignore. Finally, and maybe most importantly, we present our results according to a carefully designed, repeatable evaluation scheme and make publicly available a set of 1.7 million images with ground truth labels, geotags, and calibration data, as well as a difficult set of cell phone query images. We provide these resources as a benchmark to facilitate further research in the area. David M. Chen, Georges Baatz, Kevin Köser, Sam S. Tsai, Ramakrishna Vedantham, Timo Pylvänäinen, Kimmo Roimela, Jeff Bach, Marc Pollefeys, Bernd Girod, Radek Grzeszczuk |
CVPR | 7 |
| 2008 | Efficient high dynamic range texture compressionabstractWe present a novel compression method for high dynamic range (HDR) textures, targeted for future graphics hardware. Identifying that the existing solutions provide either very high image quality or very simple hardware implementation, we aim to achieve both features in a single solution. Kimmo Roimela, Tomi Aarnio, Joonas Itäranta |
SI3D | 1 |
| 2006 | High dynamic range texture compressionabstractWe present a novel compression scheme for high dynamic range textures, targeted for hardware implementation. Our method encodes images at a constant 8 bits per pixel, for a compression ratio of 6:1. We demonstrate that our method achieves good visual fidelity, surpassing DXTC texture compression of RGBE data which is the most practical method on existing graphics hardware. The decoding logic for our method is simple enough to be implemented as part of the texture fetch unit in graphics hardware. Kimmo Roimela, Tomi Aarnio, Joonas Itäranta |
ACM Trans. Graph. | 1 |
| 2003 | The evolution of perspective view in WalkMap
Riku Suomela, Kimmo Roimela, Juha Lehikoinen |
Pers. Ubiquitous Comput. | 2 |