Eva Eggeling

A scalable rendering framework for generative 3D content

Delivering high quality 3D content through a web browser is still a challenge especially when intellectual property (IP) protection is necessary. Thus, the transfer of 3D modeling information to a client should be avoided. In our work we present a solution to this problem by introducing a server-side rendering framework. Only images are transferred to the client, the actual 3D content is not delivered. By providing simple proxy geometry it is still possible to provide direct interaction on the client. Our framework incorporates the Generative Modeling Language (GML) for the description and rendering of generative content. It is then possible to not only interact with the 3D content, but to modify the actual shape within the possibilities of the generative content. By introducing a control layer and encapsulating processing and rendering of the generative content in a so called GML Rendering Unit (GRU) it is possible to provide a scalable rendering framework.

Visual Exploration of Large Scatter Plot Matrices by Pattern Recommendation based on Eye Tracking

The Scatter Plot Matrix (SPLOM) is a well-known technique for visual analysis of high-dimensional data. However, one problem of large SPLOMs is that typically not all views are potentially relevant to a given analysis task or user. The matrix itself may contain structured patterns across the dimensions, which could interfere with the investigation for unexplored views. We introduce a new concept and prototype implementation for an interactive recommender system supporting the exploration of large SPLOMs based on indirectly obtained user feedback from user eye tracking. Our system records the patterns that are currently under exploration based on gaze times, recommending areas of the SPLOM containing potentially new, unseen patterns for successive exploration. We use an image-based dissimilarity measure to recommend patterns that are visually dissimilar to previously seen ones, to guide the exploration in large SPLOMs. The dynamic exploration process is visualized by an analysis provenance heatmap, which captures the duration on explored and recommended SPLOM areas. We demonstrate our exploration process by a user experiment, showing the indirectly controlled recommender system achieves higher pattern recall as compared to fully interactive navigation using mouse operations.

Geometry simplification according to semantic constraints

The building industry and facility management is in a state of upheaval: The complexity of the real world is now represented in its digital counterpart. The established object-based file format “Industrial Foundation Classes (IFC)” developed by the International Alliance for Interoperability facilitates interoperability in the context of Building Information Modeling. Unfortunately, there is no feasible workflow for filtering energy-related information, e.g. a streamlined version of the building geometry. Simplification methods often fail on CAD data that is ignorant of domain specific semantic information (i.e. functional differences between a door and stucco are not reflected in the geometry and are therefore often ignored). With EU law now requiring energy performance certificates to be issued for all buildings, energy performance analysis becomes an increasingly important topic. Accurate, yet efficient calculation depends on simple building models. However, typical IFC models contain a lot of irrelevant data, in particular geometric representations, which are too detailed for energy performance analysis. Therefore, we propose an algorithm that extracts input models suitable for calculations directly from IFC models in a semi-automatic process. The key aspect of the algorithm is geometry simplification subject to semantic and functional groups; more specifically, the 3D representations of walls, slabs, windows, doors, etc. are reduced to a collection of surfaces describing the building’s thermal shell on one hand, and the material layers associated with it on the other hand. This simplification takes into account semantic constraints and expert knowledge. Furthermore, it works on “real-world” data; i.e. it is robust towards incomplete, imperfect and inconsistent data.

Sixth sense - air traffic control prediction scenario augmented by sensors

This paper is focused on the fault tolerance of Human Machine Interfaces in the field of air traffic control (ATC) by accepting the overall users body language as input. We describe ongoing work in progress in the project called Sixth Sense. Interaction patterns are reasoned from the combination of a recommendation and inference engine, the analysis of several graph database relationships and from multiple sensor raw data aggregations. Altogether, these techniques allow us to judge about different possible meanings of the current users interaction and cognitive state. The results obtained from applying different machine learning techniques will be used to make recommendations and predictions on the users actions. They are currently monitored and rated by a human supervisor.

Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis

We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.

Embedding Biomolecular Information in a Scene Graph System

We present the Bio Scene Graph (BioSG) for visualization of biomolecular structures based on the scene graph system OpenSG. The hierarchical model of primary, secondary and tertiary structures of molecules used in the organic chemistry is mapped to a graph of nodes when loading molecular files.

“Tangible Culture” --Designing virtual exhibitions on multi-touch devices

Cultural heritage institutions such as galleries, museums and libraries increasingly use digital media to present artifacts to their audience and enable them to immerse themselves in a cultural virtual world. With the application eXhibition:editor3D museum curators and editors have a software tool at hand to interactively plan and visualize exhibitions. In this paper we present an extension to the application that enhances the workflow when designing exhibitions. By introducing multi-touch technology to the graphical user interfaces the designing phase of an exhibition is efficiently simplified, especially for non-technical users. Furthermore, multi-touch technology offers a novel way of collaborative work to be integrated into a decision making process. A flexible export system allows to store created exhibitions in various formats to display them on websites, mobile devices or custom viewers. For example, the widespread 3D scene standard Extensible 3D X3D is one of the export formats and we use it to directly incorporate a realtime preview of the exhibition in the authoring process. The combination of the tangible user interfaces with the realtime preview gives curators and exhibition planers a capable tool for efficiently presenting cultural heritage in electronic media.