Dr. KID is an algorithm that uses isometric decomposition for the physicalization of potato-shaped organic models in a puzzle fashion. The algorithm begins with creating a simple, regular triangular surface mesh of organic shapes, followed by iterative k-means clustering and remeshing. For clustering, we need similarity between triangles (segments) which is defined as a distance function. The distance function maps each triangle's shape to a single point in the virtual 3D space. Thus, the distance between the triangles indicates their degree of dissimilarity. K-means clustering uses this distance and sorts of segments into k classes. After this, remeshing is applied to minimize the distance between triangles within the same cluster by making their shapes identical. Clustering and remeshing are repeated until the distance between triangles in the same cluster reaches an acceptable threshold. We adopt a curvature-aware strategy to determine the surface thickness and finalize puzzle pieces for 3D printing. Identical hinges and holes are created for assembling the puzzle components. For smoother outcomes, we use triangle subdivision along with curvature-aware clustering, generating curved triangular patches for 3D printing. Our algorithm was evaluated using various models, and the 3D-printed results were analyzed. Findings indicate that our algorithm performs reliably on target organic shapes with minimal loss of input geometry.

pdf/dr-kid-direct-remeshing-and-k-set-isometric-decomposition-237pipby.pdf

Dr. KID: Direct Remeshing and K-set Isometric Decomposition for Scalable Physicalization of Organic Shapes

In recent years, the physicalization area has expanded rapidly and has been utilized in numerous contexts, mostly in big open public areas or data-related places. Most data physicalizations have visual restrictions and no interaction. Hence, multiple technologies, including augmented reality (AR), have been applied to physical visualizations to address the above-mentioned issues. This research proposes dynamic data physicalization using AR virtual content (visual data items). The dynamic bar chart allows the configuration of numerical data to height, categorical data to color, and categorical data to the x-axis. The mobile augmented reality (MAR) application performs some Infovis tasks, such as settings, filters, details on demand, etc. A cloud server selects data, calculates visual elements or additional visualizations, calculates scales for physicalizing data, and enables the communication between the MAR application and dynamic physicalization. Lastly, dynamic and augmented data physicalization characteristics and scenarios are shown.

Adding Visual Data and Interactions for Dynamic Data Physicalization with Augmented Reality

The process of creating physical data visualizations is not a trivial task. In general, it may require skills from the user in information visualization, tangible interaction, 3D modeling, fabrication of physical objects, etc. In addition, few works have presented computational support to the entire digital and physical rendering pipeline of data visualization, characterizing many steps of this process as manual. From this context, this work presents a process that facilitates the generation of physical data visualization. Besides that, It allows one to define which type of physical visualization to create, among passive, rearrangeable, and dynamic physicalization types. Finally, the pipelines for each physicalization type are presented, with scenarios including physical bar charts, stacked bar charts, and grouped bar charts.

A Flexible Pipeline to Create Different Types of Data Physicalizations

This paper aims to present the design process of dynamic data physicalization for bar charts and their variants, simple bars, stacked bars, and clustered bars. The physical artifact has 12 bars that are automatically configured according to the dataset by electromechanical components controlled by an Arduino board. The models of the printed 3D parts, the electromechanical components used and their connection scheme, the design aspects to set up the dynamic physical visualization according to the data, how the users choose the dataset and the kind of physical chart, usage scenarios with different types of charts and physicalization parameters are presented in detail. Finally, some strengths and difficulties in creating dynamic physical visualizations are highlighted and future works are proposed.

An Overview of the Design and Development for Dynamic and Physical Bar Charts

The analysis of oceanographic data is a difficult task due to the large volume and multidimensionality of the data. Often is it necessary to aggregate other datasets to establish relationships and more complex analysis in order to answer environmental questions. Thus, this work aims to present different information visualization techniques (InfoVis) for oceanographic data concerning the most frequently used techniques, such as 2D and 3D Charts, Surface Map, Choropleth Map, and Line Map. For this purpose, a dataset of oceanographic buoys from the National Oceanic and Atmospheric Administration (NOAA) covering a period of 02 years (from 2015 to 2016) was used. This set of data was treated and classified according to the seasonal periods (winter, spring, summer, and autumn). The Rose Wind technique revealed that the wind speed average was between 10- 12 m/s. The Circle Parking technique showed a predominance of wind and waves from NE. In 2015, highest waves were recorded, mainly during the winter. The obtained results can be considered a useful tool for the assessment of the wind and waves relationship and help to coastal management strategies.

Visual Analysis Scenarios for Oceanographic Buoy Data

Computational Support to Apply Eladeb Auto-Evaluation in Multiple Platforms

The value of good data visualization has already been shown in several scenarios. Still, it is not always easy to obtain it, as it depends on factors such as the dataset, the amount of data, task types, the user profile, the type of interaction, etc. To mitigate the challenges addressed, automated or semi-automated systems have been proposed, emphasizing rule-based/heuristic approaches and machine-learning models. However, many of these applications require specialized knowledge and present results (data visualizations) that are not flexible for customization. Papers have highlighted the ease of tools like ChatGPT in creating various tasks, including creating data charts. This facility, in addition to the intelligent computational model involved, is also due to the expressiveness used in the requests to execute the tasks by the users since these tools use Natural Language Interfaces. Despite adopting these tools overgrowing in different scenarios of society, studies on the best way to use them, integrate them into existing processes, or evaluative studies on their effectiveness or efficiency are still incipient. Thus, this paper will evaluate the workload for creating data visualization using ChatGPT 3.5. For assessment, the NASA Task Load Index (Nasa TLX) methodology was applied, and users with experience creating data visualization created two proposed scenarios. The preliminary results showed high temporal and mental demand, mainly due to the vocabulary used and the completeness of the user instructions. The average time to create and perform InfoVis tasks in two proposed evaluation scenarios was 33 and 44 minutes, and 14 queries were applied on average for both scenarios. The direct consequence was that the users have redone the requests and improved the instructions at each new iteration, and all users completed the proposed tasks.

Workload Evaluation to Create Data Visualization Using ChatGPT

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