EuroVisShort2023

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633339

Browse

Now showing 1 - 6 of 6

Accelerated Volume Rendering with Volume Guided Neural Denoising
(The Eurographics Association, 2023) Jabbireddy, Susmija; Li, Shuo; Meng, Xiaoxu; Terrill, Judith E.; Varshney, Amitabh; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Monte Carlo path tracing techniques create stunning visualizations of volumetric data. However, a large number of computationally expensive light paths are required for each sample to produce a smooth and noise-free image, trading performance for quality. High-quality interactive volume rendering is valuable in various fields, especially education, communication, and clinical diagnosis. To accelerate the rendering process, we combine learning-based denoising techniques with direct volumetric rendering. Our approach uses additional volumetric features that improve the performance of the denoiser in the post-processing stage. We show that our method significantly improves the quality of Monte Carlo volume-rendered images for various datasets through qualitative and quantitative evaluation. Our results show that we can achieve volume rendering quality comparable to the state-of-the-art at a significantly faster rate using only one sample path per pixel.
ARrow: A Real-Time AR Rowing Coach
(The Eurographics Association, 2023) Iannucci, Elena; Chen, Zhutian; Armeni, Iro; Pollefeys, Marc; Pfister, Hanspeter; Beyer, Johanna; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Rowing requires physical strength and endurance in athletes as well as a precise rowing technique. The ideal rowing stroke is based on biomechanical principles and typically takes years to master. Except for time-consuming video analysis after practice, coaches currently have no means to quantitatively analyze a rower's stroke sequence and body movement. We propose ARrow, an AR application for coaches and athletes that provides real-time and situated feedback on a rower's body position and stroke. We use computer vision techniques to extract the rower's 3D skeleton and to detect the rower's stroke cycle. ARrow provides visual feedback on three levels: Tracking of basic performance metrics over time, visual feedback and guidance on a rower's stroke sequence, and a rowing ghost view that helps synchronize the body movement of two rowers. We developed ARrow in close colaboration with international rowing coaches and demonstrate its usefulness in a user study with athletes and coaches.
Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT's Potential to Apply Graph Layout Algorithms
(The Eurographics Association, 2023) Bartolomeo, Sara Di; Severi, Giorgio; Schetinger, Victor; Dunne, Cody; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Large language models (LLMs) have recently taken the world by storm. They can generate coherent text, hold meaningful conversations, and be taught concepts and basic sets of instructions-such as the steps of an algorithm. In this context, we are interested in exploring the application of LLMs to graph drawing algorithms by performing experiments on ChatGPT, one of the most recent cutting-edge LLMs made available to the public. These algorithms are used to create readable graph visualizations. The probabilistic nature of LLMs presents challenges to implementing algorithms correctly, but we believe that LLMs' ability to learn from vast amounts of data and apply complex operations may lead to interesting graph drawing results. For example, we could enable users with limited coding backgrounds to use simple natural language to create effective graph visualizations. Natural language specification would make data visualization more accessible and user-friendly for a wider range of users. Exploring LLMs' capabilities for graph drawing can also help us better understand how to formulate complex algorithms for LLMs; a type of knowledge that could transfer to other areas of computer science. Overall, our goal is to shed light on the exciting possibilities of using LLMs for graph drawing-using the Sugiyama algorithm as a sample case-while providing a balanced assessment of the challenges and opportunities they present. A free copy of this paper with all supplemental materials to reproduce our results is available on osf.io .
Multi-attribute Visualization and Improved Depth Perception for the Interactive Analysis of 3D Truss Structures
(The Eurographics Association, 2023) Becher, Michael; Groß, Anja; Werner, Peter; Maierhofer, Mathias; Reina, Guido; Ertl, Thomas; Menges, Achim; Weiskopf, Daniel; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
In architecture, engineering, and construction (AEC), load-bearing truss structures are commonly modeled as a set of connected beam elements. For complex 3D structures, rendering beam elements as line segments presents several challenges due densely overlapping elements, including visual clutter, and general depth perception issues. Furthermore, line segments provide very little area for displaying additional element attributes. In this paper, we investigate the effectiveness of rendering effects for reducing visual clutter and improving depth perception for truss structures specifically, such as distance-based brightness attenuation and screen-space ambient occlusion (SSAO). Additionally, we provide multiple options for multi-attribute visualization directly on the structure and evaluate both aspects with two expert interviews.
RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings
(The Eurographics Association, 2023) Morgenshtern, Gabriela; Verma, Arnav; Tonekaboni, Sana; Greer, Robert; Bernard, Jürgen; Mazwi, Mjaye; Goldenberg, Anna; Chevalier, Fanny; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Many real-world machine learning workflows exist in longitudinal, interactive machine learning (ML) settings. This longitudinal nature is often due to incremental increasing of data, e.g., in clinical settings, where observations about patients evolve over their care period. Additionally, experts may become a bottleneck in the workflow, as their limited availability, combined with their role as human oracles, often leads to a lack of ground truth data. In such cases where ground truth data is small, the validation of interactive machine learning workflows relies on domain experts. Only those humans can assess the validity of a model prediction, especially in new situations that have been covered only weakly by available training data. Based on our experiences working with domain experts of a pediatric hospital's intensive care unit, we derive requirements for the design of support interfaces for the validation of interactive ML workflows in fast-paced, high-intensity environments. We present RiskFix, a software package optimized for the validation workflow of domain experts of such contexts. RiskFix is adapted to the cognitive resources and needs of domain experts in validating and giving feedback to the model. Also, RiskFix supports data scientists in their model-building work, with appropriate data structuring for the re-calibration (and possible retraining) of ML models.
Visual Exploration of Indirect Bias in Language Models
(The Eurographics Association, 2023) Louis-Alexandre, Judith; Waldner, Manuela; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Language models are trained on large text corpora that often include stereotypes. This can lead to direct or indirect bias in downstream applications. In this work, we present a method for interactive visual exploration of indirect multiclass bias learned by contextual word embeddings. We introduce a new indirect bias quantification score and present two interactive visualizations to explore interactions between multiple non-sensitive concepts (such as sports, occupations, and beverages) and sensitive attributes (such as gender or year of birth) based on this score.

Browse

Browsing EuroVisShort2023 by Subject "Computing methodologies"

Results Per Page

Sort Options