@article{ScheibelTrappLimbergeretal.2020, author = {Scheibel, Willy and Trapp, Matthias and Limberger, Daniel and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {A taxonomy of treemap visualization techniques}, series = {Science and Technology Publications}, journal = {Science and Technology Publications}, publisher = {Springer}, address = {Berlin}, pages = {8}, year = {2020}, abstract = {A treemap is a visualization that has been specifically designed to facilitate the exploration of tree-structured data and, more general, hierarchically structured data. The family of visualization techniques that use a visual metaphor for parent-child relationships based "on the property of containment" (Johnson, 1993) is commonly referred to as treemaps. However, as the number of variations of treemaps grows, it becomes increasingly important to distinguish clearly between techniques and their specific characteristics. This paper proposes to discern between Space-filling Treemap TS, Containment Treemap TC, Implicit Edge Representation Tree TIE, and Mapped Tree TMT for classification of hierarchy visualization techniques and highlights their respective properties. This taxonomy is created as a hyponymy, i.e., its classes have an is-a relationship to one another: TS TC TIE TMT. With this proposal, we intend to stimulate a discussion on a more unambiguous classification of treemaps and, furthermore, broaden what is understood by the concept of treemap itself.}, language = {en} } @article{VollmerTrappSchumannetal.2018, author = {Vollmer, Jan Ole and Trapp, Matthias and Schumann, Heidrun and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {Hierarchical spatial aggregation for level-of-detail visualization of 3D thematic data}, series = {ACM transactions on spatial algorithms and systems}, volume = {4}, journal = {ACM transactions on spatial algorithms and systems}, number = {3}, publisher = {Association for Computing Machinery}, address = {New York}, issn = {2374-0353}, doi = {10.1145/3234506}, pages = {23}, year = {2018}, abstract = {Thematic maps are a common tool to visualize semantic data with a spatial reference. Combining thematic data with a geometric representation of their natural reference frame aids the viewer's ability in gaining an overview, as well as perceiving patterns with respect to location; however, as the amount of data for visualization continues to increase, problems such as information overload and visual clutter impede perception, requiring data aggregation and level-of-detail visualization techniques. While existing aggregation techniques for thematic data operate in a 2D reference frame (i.e., map), we present two aggregation techniques for 3D spatial and spatiotemporal data mapped onto virtual city models that hierarchically aggregate thematic data in real time during rendering to support on-the-fly and on-demand level-of-detail generation. An object-based technique performs aggregation based on scene-specific objects and their hierarchy to facilitate per-object analysis, while the scene-based technique aggregates data solely based on spatial locations, thus supporting visual analysis of data with arbitrary reference geometry. Both techniques can apply different aggregation functions (mean, minimum, and maximum) for ordinal, interval, and ratio-scaled data and can be easily extended with additional functions. Our implementation utilizes the programmable graphics pipeline and requires suitably encoded data, i.e., textures or vertex attributes. We demonstrate the application of both techniques using real-world datasets, including solar potential analyses and the propagation of pressure waves in a virtual city model.}, language = {en} } @article{StojanovicTrappRichteretal.2019, author = {Stojanovic, Vladeta and Trapp, Matthias and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {Service-oriented semantic enrichment of indoor point clouds using octree-based multiview classification}, series = {Graphical Models}, volume = {105}, journal = {Graphical Models}, publisher = {Elsevier}, address = {San Diego}, issn = {1524-0703}, doi = {10.1016/j.gmod.2019.101039}, pages = {18}, year = {2019}, abstract = {The use of Building Information Modeling (BIM) for Facility Management (FM) in the Operation and Maintenance (O\&M) stages of the building life-cycle is intended to bridge the gap between operations and digital data, but lacks the functionality of assessing the state of the built environment due to non-automated generation of associated semantics. 3D point clouds can be used to capture the physical state of the built environment, but also lack these associated semantics. A prototypical implementation of a service-oriented architecture for classification of indoor point cloud scenes of office environments is presented, using multiview classification. The multiview classification approach is tested using a retrained Convolutional Neural Network (CNN) model - Inception V3. The presented approach for classifying common office furniture objects (chairs, sofas and desks), contained in 3D point cloud scans, is tested and evaluated. The results show that the presented approach can classify common office furniture up to an acceptable degree of accuracy, and is suitable for quick and robust semantics approximation - based on RGB (red, green and blue color channel) cubemap images of the octree partitioned areas of the 3D point cloud scan. Additional methods for web-based 3D visualization, editing and annotation of point clouds are also discussed. Using the described approach, captured scans of indoor environments can be semantically enriched using object annotations derived from multiview classification results. Furthermore, the presented approach is suited for semantic enrichment of lower resolution indoor point clouds acquired using commodity mobile devices.}, language = {en} } @article{ReimannKlingbeilPasewaldtetal.2019, author = {Reimann, Max and Klingbeil, Mandy and Pasewaldt, Sebastian and Semmo, Amir and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {Locally controllable neural style transfer on mobile devices}, series = {The Visual Computer}, volume = {35}, journal = {The Visual Computer}, number = {11}, publisher = {Springer}, address = {New York}, issn = {0178-2789}, doi = {10.1007/s00371-019-01654-1}, pages = {1531 -- 1547}, year = {2019}, abstract = {Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. In this work, we first propose a problem characterization of interactive style transfer representing a trade-off between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, we enhance state-of-the-art neural style transfer techniques by mask-based loss terms that can be interactively parameterized by a generalized user interface to facilitate a creative and localized editing process. We report on a usability study and an online survey that demonstrate the ability of our app to transfer styles at improved semantic plausibility.}, language = {en} } @article{BuschmannTrappDoellner2016, author = {Buschmann, Stefan and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich}, title = {Animated visualization of spatial-temporal trajectory data for air-traffic analysis}, series = {The Visual Computer}, volume = {32}, journal = {The Visual Computer}, publisher = {Springer}, address = {New York}, issn = {0178-2789}, doi = {10.1007/s00371-015-1185-9}, pages = {371 -- 381}, year = {2016}, abstract = {With increasing numbers of flights worldwide and a continuing rise in airport traffic, air-traffic management is faced with a number of challenges. These include monitoring, reporting, planning, and problem analysis of past and current air traffic, e.g., to identify hotspots, minimize delays, or to optimize sector assignments to air-traffic controllers. To cope with these challenges, cyber worlds can be used for interactive visual analysis and analytical reasoning based on aircraft trajectory data. However, with growing data size and complexity, visualization requires high computational efficiency to process that data within real-time constraints. This paper presents a technique for real-time animated visualization of massive trajectory data. It enables (1) interactive spatio-temporal filtering, (2) generic mapping of trajectory attributes to geometric representations and appearance, and (3) real-time rendering within 3D virtual environments such as virtual 3D airport or 3D city models. Different visualization metaphors can be efficiently built upon this technique such as temporal focus+context, density maps, or overview+detail methods. As a general-purpose visualization technique, it can be applied to general 3D and 3+1D trajectory data, e.g., traffic movement data, geo-referenced networks, or spatio-temporal data, and it supports related visual analytics and data mining tasks within cyber worlds.}, language = {en} }