IEEE Computer Graphics and Applications最新文献

In this article, we present Public VaLUe Assessment TOol (PLUTO), a framework for assessing the public value of specific instances of data use. Grounded in the concept of data solidarity, PLUTO aims to empower diverse stakeholders-including regulatory bodies, private enterprises, nongovernmental organization (NGOs), and individuals-to critically engage with data projects through a structured assessment of the risks and benefits of data use, and by encouraging critical reflection. This article discusses the theoretical foundation, development process, and initial user experiences with PLUTO. Key challenges include translating qualitative assessments of benefits and risks into actionable quantitative metrics while maintaining inclusivity and transparency. Initial feedback highlights PLUTO's potential to foster responsible decision-making and shared accountability in data practices.

This article addresses the need for scenario-specific drone simulators to enhance research, education, pilot training, and performance evaluation across diverse mission contexts. A gamified simulator was developed for the joint master's degree program "Security and Defence" between the Open University of Cyprus and the Hellenic Air Force Academy, with a focus on search and rescue mission training and planning. The simulator balances interface design, physics accuracy, and environmental realism to support specialized professional training and research. A survey-based evaluation involving 57 participants-pilots, engineers, and air traffic controllers-demonstrated the tool's ability to provide realistic experiences for trained pilots and meaningful learning opportunities for novices. Statistical analysis revealed significant correlations between gaming familiarity, task completion times, and participant expertise.

Current data visualization research demonstrates very limited inclusion of users with language disabilities. To address this, this article introduces the language disabilities developmental language disorder (DLD) and aphasia. We present outcomes from a novel qualitative diary study exploring whether people living with either DLD or aphasia experience and engage with data visualization in their day-to-day lives. Outcomes reveal evidence of both exposure to, and engagement with, data visualization across a week-long period alongside accompanying experiences of inclusion and exclusion of the benefits of data visualization. We report types of data visualization tasks and application domains encountered and descriptions of issues experienced by participants. Findings highlight a critical need for increased awareness of language access needs within the discipline of data visualization and a case for further research into design practices inclusive of people with language disabilities.

Screen-space ambient occlusion (SSAO) has become a widely used technique in real-time rendering, valued for its high performance and full support for dynamic geometry. However, applying SSAO directly to stereo rendering can result in incorrect depth perception and viewer discomfort due to differences in captured scene approximations between the left and right views. Existing methods for generating stereo-consistent SSAO often involve substantial computational costs. This article introduces an adaptive method, inspired by Weber's law, to enhance the efficiency of achieving stereo-consistent SSAO. Our method identifies inconsistent pixels generated by cost-effective SSAO algorithms, such as monoscopic SSAO, and selectively applies computationally intensive stereo-aware computations only to those pixels. Experiments demonstrate that our method delivers stereo-consistent results comparable to state-of-the-art techniques while significantly enhancing rendering performance.

Presenting data visually is a cornerstone of effective science communication. While prior studies have investigated humans' ability to effectively perceive values in charts, fewer have focused on the translation of perceived values to real-world conclusions. Those that do focus on real-world understanding often utilize convenience samples or focus on very simple graphic formats, resulting in an incomplete understanding of how viewers translate data graphics into meaningful conclusions. We utilize a probability-based sample of over 3000 participants in the U.S. to test user understanding of three chart types and find that both educational attainment and age play a role in the ability to interpret data graphics. Our work demonstrates a need for further study on how chart comprehension and comfort with drawing real-world conclusions differ across demographic groups and commonly used chart types. In addition, this work highlights that complex charts can be inaccessible to viewers who lack confidence in reading a chart.

Research has shown that an audience's age impacts their engagement in digital media. Interactive narrative visualization is an increasingly popular form of digital media that combines data visualization and storytelling to convey important information. However, audience age is often overlooked by interactive narrative visualization authors. Using an established visualization engagement questionnaire, we ran an empirical experiment where we compared end-user engagement to audience age. We found a small difference in engagement scores where older age cohorts were less engaged than the youngest age cohort. Our qualitative analysis revealed that the terminology and overall understanding of interactive narrative patterns integrated into narrative visualization were more apparent in the feedback from younger age cohorts relative to the older age cohorts. We conclude this article with a series of recommendations for authors of interactive narrative visualization on how to design inclusively for audiences according to their age.

Accessible design for some may still produce barriers for others. This tension, called access friction, creates challenges for both designers and end-users with disabilities. To address this, we present the concept of softerware, a system design approach that provides end-users with agency to meaningfully customize and adapt interfaces to their needs. To apply softerware to visualization, we assembled 195 data visualization customization options centered on the barriers we expect users with disabilities will experience. We built a prototype that applied a subset of these options and interviewed practitioners for feedback. Lastly, we conducted a design probe study with blind and low-vision accessibility professionals to learn more about their challenges and visions for softerware. We observed access frictions between our participant's designs, and they expressed that for softerware's success, current and future systems must be designed with accessible defaults, interoperability, persistence, and respect for a user's perceived effort-to-outcome ratio.

Sketch-based or text-based 3D content generation, which rapidly converts abstract concepts into 3D models, plays a vital role in accelerating digitalization. However, the sparse sketch, absent texture and unaligned dataset pose considerable challenges for cross-modal synthesis. To address these issues, we introduce OmniSketch, a sketch-guided text-to-3D generation framework that decouples the complex sketch-to-3D task into a three-stage pipeline. Firstly, we employ a conditional diffusion model to fuse structural constraints from the sketch with semantic descriptions, generating a high-quality intermediate reference view. Subsequently, a Rectified Flow model is utilized to produce a 3D model with high-fidelity geometry. Finally, we apply a geometry-guided multi-view image synthesis method to furnish the model with view-consistent, high-quality textural details. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches in geometric fidelity and texture consistency. Leveraging our framework, we also construct a multi-modal sketch-to-3D dataset and an interactive platform to promote the application of digital content generation.

AI support in CAD is an active research field, generating an increasing number of publications. Recent surveys give a broad overview of this field, each structuring works differently, e.g., by application area. Given this magazine's and special issue's scope, we focus on research relevant to the industrial design process, excluding applications such as avatar or toy generation. Within this scope, we broaden the discussion beyond core computer graphics and artificial intelligence to include perspectives from design theory and human cognition, which clarifies CAD's role in designing and how Generative AI methods relate to designers' intelligence. We discuss CAD as part of the design process, examine what constitutes designers' intelligence, and how this relates to theories of human intelligence. We analyze current works in Generative AI for CAD and how they can be positioned in this landscape. Finally, we discuss limitations and challenges, and identify avenues for future research.

The need for large, high-quality annotated datasets continues to represent a primary limitation in training Object Detection (OD) models. To mitigate this challenge, we present VILOD, a Visual Interactive Labeling tool that integrates Active Learning (AL) with a suite of interactive visualizations to create an effective Human-in-the-Loop (HITL) workflow for OD annotation and training. VILOD is designed to make the AL process more transparent and steerable, empowering expert users to implement diverse, strategically guided labeling strategies that extend beyond algorithmic query strategies. Through comparative case studies, we evaluate three visually guided labeling strategies against a conventional automated AL baseline. The results show that a balanced, human-guided strategy-leveraging VILOD's visual cues to synthesize information about data structure and model uncertainty-not only outperforms the automated baseline but also achieves the highest overall model performance. These findings emphasize the potential of visually guided, interactive annotation to enhance both the efficiency and effectiveness of dataset creation for OD.