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Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a basis for thermal simulations to assess the building's energy performance. This paper explores the intersection of BIM and BEM, focusing on the challenges and methodologies in converting BIM data into BEM representations for energy performance analysis. BEMTrace integrates 3D data wrangling techniques with visualization methodologies to enhance the accuracy and traceability of the BIM-to-BEM conversion process. Through parsing, error detection, and algorithmic correction of BIM data, our methods generate valid BEM models suitable for energy simulation. Visualization techniques provide transparent insights into the conversion process, aiding error identification, validation, and user comprehension. We introduce context-adaptive selections to facilitate user interaction and to show that the BEMTrace workflow helps users understand complex 3D data wrangling processes., Comment: 9 pages and 2 pages references, 12 figures

Analyzing complex and large data as generated in non-destructive testing (NDT) is a time-consuming and mentally demanding challenge. Such data is heterogeneous and integrates primary and secondary derived data from materials or material systems for spatial, spatio-temporal as well as high-dimensional data analysis. Currently, materials experts mainly rely on conventional desktop systems using standard 2D visualization techniques for this purpose. Our framework is a novel immersive visual analytics system, which supports the exploration of complex spatial structures and derived multidimensional abstract data in an augmented reality setting. It includes three novel visualization techniques: MDD-Glyphs, TimeScatter, and ChronoBins, each facilitating the interactive exploration and comparison of multidimensional distributions from multiple datasets and time steps. A qualitative evaluation conducted with materials experts and novices in a real-world case study demonstrated the benefits of the proposed visualization techniques. This evaluation also revealed that combining spatial and abstract data in an immersive environment improved their analytical capabilities and facilitated to better and faster identify patterns, anomalies, as well as changes over time., Comment: 14 pages, 9 figures

We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. Octree approaches work by performing hierarchical tree traversal. However, in octree volume rendering, tree traversal and the selection of data resolution are intrinsically coupled. This makes fine-grained empty-space skipping costly. Page tables, on the other hand, allow access to any cached brick from any resolution. However, they do not offer a clear and efficient strategy for substituting missing high-resolution data with lower-resolution data. We enable flexible mixed-resolution out-of-core multi-volume rendering by decoupling the cache residency of multi-resolution data from a resolution-independent spatial subdivision determined by the tree. Instead of one-to-one node-to-brick correspondences, each residency octree node is mapped to a set of bricks from different resolution levels. This makes it possible to efficiently and adaptively choose and mix resolutions, adapt sampling rates, and compensate for cache misses. At the same time, residency octrees support fine-grained empty-space skipping, independent of the data subdivision used for caching. Finally, to facilitate collaboration and outreach, and to eliminate local data storage, our implementation is a web-based, pure client-side renderer using WebGPU and WebAssembly. Our method is faster than prior approaches and efficient for many data channels with a flexible and adaptive choice of data resolution., Comment: VIS 2023 - full paper

Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we present a framework for interactive, user-driven, feature-assisted geometry reconstruction from arbitrarily sized point clouds. Based on seeded region-growing point cloud segmentation, the user interactively extracts planar pieces of geometry and utilizes contextual suggestions to point out plane surfaces, normal and tangential directions, and edges and corners. We implement a set of feature-assisted tools for high-precision modeling tasks in architecture and urban surveying scenarios, enabling instant-feedback interactive point cloud manipulation on large-scale data collected from real-world building interiors and facades. We evaluate our results through systematic measurement of the reconstruction accuracy, and interviews with domain experts who deploy our framework in a commercial setting and give both structured and subjective feedback., Comment: 13 pages, submitted to Computers & Graphics Journal

Understanding and comparing distributions of data (e.g., regarding their modes, shapes, or outliers) is a common challenge in many scientific disciplines. Typically, this challenge is addressed using side-by-side comparisons of histograms or density plots. However, comparing multiple density plots is mentally demanding. Uniform histograms often represent distributions imprecisely since missing values, outliers, or modes are hidden by a grouping of equal size. In this paper, a novel type of overview visualization for the comparison of univariate data distributions is presented: AccuStripes (i.e., accumulated stripes) is a new visual metaphor encoding accumulations of data distributions according to adaptive binning using color coded stripes of irregular width. We provide detailed insights about challenges of binning. Specifically, we explore different adaptive binning concepts such as Bayesian Blocks binning and Jenks Natural Breaks binning for the computation of binning boundaries, in terms of their capabilities to represent the datasets as accurately as possible. In addition, we discuss issues arising with the representation of designs for the comparative visualization of distributions: To allow for a comparison of many distributions, their accumulated representations are plotted below each other in a stacked mode. Based on our findings, we propose three different layouts for comparative visualization of multiple distributions. The usefulness of AccuStripes is investigated using a statistical evaluation of the binning methods. Using a similarity metric from cluster analysis, it is shown, which binning method statistically yields the best grouping results. Through a user study we evaluate, which binning strategy visually represents the distribution in the most intuitive form and investigate, which layout allows the user the comparison of many distributions in the most effortless way., Comment: 11 pages, 6 figures

Geological analysis of 3D Digital Outcrop Models (DOMs) for reconstruction of ancient habitable environments is a key aspect of the upcoming ESA ExoMars 2022 Rosalind Franklin Rover and the NASA 2020 Rover Perseverance missions in seeking signs of past life on Mars. Geologists measure and interpret 3D DOMs, create sedimentary logs and combine them in `correlation panels' to map the extents of key geological horizons, and build a stratigraphic model to understand their position in the ancient landscape. Currently, the creation of correlation panels is completely manual and therefore time-consuming, and inflexible. With InCorr we present a visualization solution that encompasses a 3D logging tool and an interactive data-driven correlation panel that evolves with the stratigraphic analysis. For the creation of InCorr we closely cooperated with leading planetary geologists in the form of a design study. We verify our results by recreating an existing correlation analysis with InCorr and validate our correlation panel against a manually created illustration. Further, we conducted a user-study with a wider circle of geologists. Our evaluation shows that InCorr efficiently supports the domain experts in tackling their research questions and that it has the potential to significantly impact how geologists work with digital outcrop representations in general.