IEEE transactions on visualization and computer graphics最新文献

Incorporating automatic style extraction and transfer from existing well-designed graph visualizations can significantly alleviate the designer's workload. There are many types of graph visualizations. In this paper, our work focuses on node-link diagrams. We present a novel approach to streamline the design process of graph visualizations by automatically extracting visual styles from well-designed examples and applying them to other graphs. Our formative study identifies the key styles that designers consider when crafting visualizations, categorizing them into global and local styles. Leveraging deep learning techniques such as saliency detection models and multi-label classification models, we develop end-to-end pipelines for extracting both global and local styles. Global styles focus on aspects such as color scheme and layout, while local styles are concerned with the finer details of node and edge representations. Through a user study and evaluation experiment, we demonstrate the efficacy and time-saving benefits of our method, highlighting its potential to enhance the graph visualization design process.

Accurate segmentation of 3D point clouds in indoor scenes remains a challenging task, often hindered by the labor-intensive nature of data annotation. While weakly supervised learning approaches have shown promise in leveraging partial annotations, they frequently struggle with imbalanced performance between foreground and background elements due to the complex structures and proximity of objects in indoor environments. To address this issue, we propose a novel foreground-aware label enhancement method utilizing visual boundary priors. Our approach projects 3D point clouds onto 2D planes and applies 2D image segmentation to generate pseudo-labels for foreground objects. These labels are subsequently back-projected into 3D space and used to train an initial segmentation model. We further refine this process by incorporating prior knowledge from projected images to filter the predicted labels, followed by model retraining. We introduce this technique as the Foreground Boundary Prior (FBP), a versatile, plug-and-play module designed to enhance various weakly supervised point cloud segmentation methods. We demonstrate the efficacy of our approach on the widely-used 2D-3D-Semantic dataset, employing both random-sample and bounding-box based weak labeling strategies. Our experimental results show significant improvements in segmentation performance across different architectural backbones, highlighting the method's effectiveness and portability.

The transfer function (TF) design is crucial for enhancing the visualization quality and understanding of volume data in volume rendering. Recent research has proposed various multidimensional TFs to utilize diverse attributes extracted from volume data for controlling individual voxel rendering. Although multidimensional TFs enhance the ability to segregate data, manipulating various attributes for the rendering is cumbersome. In contrast, low-dimensional TFs are more beneficial as they are easier to manage, but separating volume data during rendering is problematic. This paper proposes a novel approach, a two-level transfer function, for rendering volume data by reducing TF dimensions. The proposed technique involves extracting multidimensional TF attributes from volume data and applying t-Stochastic Neighbor Embedding (t-SNE) to the TF attributes for dimensionality reduction. The two-level transfer function combines the classical 2D TF and t-SNE TF in the conventional direct volume rendering pipeline. The proposed approach is evaluated by comparing segments in t-SNE TF and rendering images using various volume datasets. The results of this study demonstrate that the proposed approach can effectively allow us to manipulate multidimensional attributes easily while maintaining high visualization quality in volume rendering.

Measuring the 3D shape of semi-transparent surfaces with projector-camera 3D scanners is a difficult task because these surfaces weakly reflect light in a diffuse manner, and transmit a large part of the incident light. The task is even harder in the presence of participating background surfaces. The two methods proposed in this paper use sinusoidal patterns, each with a frequency chosen in the frequency range allowed by the projection optics of the projector-camera system. They differ in the way in which the camera-projector correspondence map is established, as well as in the number of patterns and the processing time required. The first method utilizes the discrete Fourier transform, performed on the intensity signal measured at a camera pixel, to inventory projector columns illuminating directly and indirectly the scene point imaged by that pixel. The second method goes beyond discrete Fourier transform and achieves the same goal by fitting a proposed analytical model to the measured intensity signal. Once the one (camera pixel) to many (projector columns) correspondence is established, a surface continuity constraint is applied to extract the one to one correspondence map linked to the semi-transparent surface. This map is used to determine the 3D point cloud of the surface by triangulation. Experimental results demonstrate the performance (accuracy, reliability) achieved by the proposed methods.

We present a novel data-driven Parametric Linear Blend Skinning (PLBS) model meticulously crafted for generalized 3D garment dressing and animation. Previous data-driven methods are impeded by certain challenges including overreliance on human body modeling and limited adaptability across different garment shapes. Our method resolves these challenges via two goals: 1) Develop a model based on garment modeling rather than human body modeling. 2) Separately construct low-dimensional sub-spaces for modeling in-plane deformation (such as variation in garment shape and size) and out-of-plane deformation (such as deformation due to varied body size and motion). Therefore, we formulate garment deformation as a PLBS model controlled by canonical 3D garment mesh, vertex-based skinning weights and associated local patch transformation. Unlike traditional LBS models specialized for individual objects, PLBS model is capable of uniformly expressing varied garments and bodies, the in-plane deformation is encoded on the canonical 3D garment and the out-of-plane deformation is controlled by the local patch transformation. Besides, we propose novel 3D garment registration and skinning weight decomposition strategies to obtain adequate data to build PLBS model under different garment categories. Furthermore, we employ dynamic fine-tuning to complement high-frequency signals missing from LBS for unseen testing data. Experiments illustrate that our method is capable of modeling dynamics for loose-fitting garments, outperforming previous data-driven modeling methods using different sub-space modeling strategies. We showcase that our method can factorize and be generalized for varied body sizes, garment shapes, garment sizes and human motions under different garment categories.

Cybersickness, or sickness induced by virtual reality (VR), negatively impacts the enjoyment and adoption of the technology. One method that has been used to reduce sickness is repeated exposure to VR, herein Cybersickness Abatement from Repeated Exposure (CARE). However, high sickness levels during repeated exposure may discourage some users from returning. Field of view (FOV) restriction reduces cybersickness by minimizing visual motion in the periphery, but also negatively affects the user's visual experience. This study explored whether CARE that occurs with FOV restriction generalizes to a full FOV experience. Participants played a VR game for up to 20 minutes. Those in the Repeated Exposure Condition played the same VR game on four separate days, experiencing FOV restriction during the first three days and no FOV restriction on the fourth day. Results indicated significant CARE with FOV restriction (Days 1-3). Further, cybersickness on Day 4, without FOV restriction, was significantly lower than that of participants in the Single Exposure Condition, who experienced the game without FOV restriction only on one day. The current findings show that significant CARE can occur while experiencing minimal cybersickness. Results are considered in the context of multiple theoretical explanations for CARE, including sensory rearrangement, adaptation, habituation, and postural control.

Contour maps are an essential tool for exploring spatial features of the terrain, such as distance, directions, and surface gradient among the contour areas. User interactions in contour-based visualizations create approaches to visual analysis that are noticeably different from the perspective of human cognition. As such, various interactive approaches have been introduced to improve system usability and enhance human cognition for complex and large-scale spatial data exploration. However, what user interaction means for contour maps, its purpose, when to leverage, and design primitives have yet to be investigated in the context of analysis tasks. Therefore, further research is needed to better understand and quantify the potentials and benefits offered by user interactions in contour-based geospatial visualizations designed to support analytical tasks. In this paper, we present a contour-based interactive geospatial visualization designed for analytical tasks. We conducted a crowd-sourced user study (N=62) to examine the impact of interactive features on analysis using contour-based geospatial visualizations. Our results show that the interactive features aid in their data analysis and understanding in terms of spatial data extent, map layout, task complexity, and user expertise. Finally, we discuss our findings in-depth, which will serve as guidelines for future design and implementation of interactive features in support of case-specific analytical tasks on contour-based geospatial views.

Creating data videos that effectively narrate stories with animated visuals requires substantial effort and expertise. A promising research trend is leveraging the easy-to-use natural language (NL) interaction to automatically synthesize data video components from narrative content like text narrations, or NL commands that specify user-required designs. Nevertheless, previous research has overlooked the integration of narrative content and specific design authoring commands, leading to generated results that lack customization or fail to seamlessly fit into the narrative context. To address these issues, we introduce a novel paradigm for creating data videos, which seamlessly integrates users' authoring and narrative intents in a unified format called annotated narration, allowing users to incorporate NL commands for design authoring as inline annotations within the narration text. Informed by a formative study on users' preference for annotated narration, we develop a prototype system named Data Playwright that embodies this paradigm for effective creation of data videos. Within Data Playwright, users can write annotated narration based on uploaded visualizations. The system's interpreter automatically understands users' inputs and synthesizes data videos with narration-animation interplay, powered by large language models. Finally, users can preview and fine-tune the video. A user study demonstrated that participants can effectively create data videos with Data Playwright by effortlessly articulating their desired outcomes through annotated narration.

Reconstructing CSG trees from CAD models is a critical subject in reverse engineering. While there have been notable advancements in CSG reconstruction, challenges persist in capturing geometric details and achieving efficiency. Additionally, since non-axis-aligned volumetric primitives cannot maintain coplanar characteristics due to discretization errors, existing Boolean operations often lead to zero-volume surfaces and suffer from topological errors during the CSG modeling process. To address these issues, we propose a novel workflow to achieve fast CSG reconstruction and reliable forward modeling. First, we employ feature removal and model subdivision techniques to decompose models into sub-components. This significantly expedites the reconstruction by simplifying the complexity of the models. Then, we introduce a more reasonable method for primitive generation and filtering, and utilize a size-related optimization approach to reconstruct CSG trees. By re-adding features as additional nodes in the CSG trees, our method not only preserves intricate details but also ensures the conciseness, semantic integrity, and editability of the resulting CSG tree. Finally, we develop a coplanar primitive discretization method that represents primitives as large planes and extracts the original triangles after intersection. We extend the classification of triangles and incorporate a coplanar-aware Boolean tree assessment technique, allowing us to achieve manifold and watertight modeling results without zero-volume surfaces, even in extreme degenerate cases. We demonstrate the superiority of our method over state-of-the-art approaches. Moreover, the reconstructed CSG trees generated by our method contain extensive semantic information, enabling diverse model editing tasks.