Computers & Graphics-Uk最新文献_第2页

Foreword to the special section on eXtended Reality for Industrial and Occupational Supports (XRIOS) 工业和职业支持的扩展现实（XRIOS）特别部分前言

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-15 DOI: 10.1016/j.cag.2025.104242

Isaac Cho, Heejin Jeong, Kangsoo Kim, Hyungil Kim, Myounghoon Jeon

引用次数: 0

HAXplorer: Interactive visual exploration of hierarchical item and attribute spaces HAXplorer：层次项目和属性空间的交互式可视化探索

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-13 DOI: 10.1016/j.cag.2025.104233

Michael Blum , Jonas Blum , Madhav Sachdeva , Jürgen Bernard

{"title":"HAXplorer: Interactive visual exploration of hierarchical item and attribute spaces","authors":"Michael Blum , Jonas Blum , Madhav Sachdeva , Jürgen Bernard","doi":"10.1016/j.cag.2025.104233","DOIUrl":"10.1016/j.cag.2025.104233","url":null,"abstract":"<div><div>Analyzing tabular data by leveraging hierarchical structures for its items and attributes is a promising approach to scale for dataset sizes that make per-item and per-attribute analysis impractical. Existing approaches face limitations in supporting both item and attribute hierarchies, enabling user-controlled hierarchy creation, and ensuring visual scalability and interaction utility. We present <em>HAXplorer</em>, a visual analytics approach that enables users to create both item and attribute hierarchies, and to explore the resulting tabular data space by leveraging item and attribute aggregates. We demonstrate the generalizability of <em>HAXplorer</em> through usage scenarios across three diverse domains and evaluate its usefulness in a task-based user study. Usability is assessed through a perceived readability questionnaire and qualitative feedback. In addition to introducing a novel visual analytics system, our work offers insights into visual literacy, design validation methodologies, the positioning of <em>HAXplorer</em> within the broader landscape of biclustering techniques, and highlights the generative power of abstraction.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"129 ","pages":"Article 104233"},"PeriodicalIF":2.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PcdGS: A point cloud densification method for Gaussian Splatting 一种高斯溅射的点云密度化方法

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-12 DOI: 10.1016/j.cag.2025.104241

Jing Zhao, Chenxi Li, Shuhui Zhang, Feng Wang, Ying Yin, Yuhan Liu

{"title":"PcdGS: A point cloud densification method for Gaussian Splatting","authors":"Jing Zhao, Chenxi Li, Shuhui Zhang, Feng Wang, Ying Yin, Yuhan Liu","doi":"10.1016/j.cag.2025.104241","DOIUrl":"10.1016/j.cag.2025.104241","url":null,"abstract":"<div><div>Novel view synthesis (NVS) from limited observations continues to be an important and persistent challenge. Currently, methods based on Neural Radiance Fields (NeRF) are not very efficient, and although methods based on 3D Gaussian Splatting (3DGS) outperform NeRF in terms of rendering quality and speed, they still lack sufficient details. To tackle these issues, we introduce a new method for densifying the point cloud, which enhances the rendering effect of 3DGS-based techniques under sparse view conditions. First, we introduce a mask-based densification technique to improve rendering details under limited input views. Second, We propose a monocular pixel depth-based mapping method that leverages a pre-trained model to predict depth, effectively normalizing point locations within the resulting point cloud. Lastly, we implement a filtering method based on depth and RGB color to minimize noise introduced by the additional data. We conduct comparative experiments on the LLFF, Mip-NeRF 360, and Blender datasets, demonstrating that our method outperforms existing approaches in both evaluation metrics and visual quality, thereby validating its effectiveness.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"130 ","pages":"Article 104241"},"PeriodicalIF":2.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational topology for hand-drawn animation technology: A survey 手绘动画技术的计算拓扑：综述

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-10 DOI: 10.1016/j.cag.2025.104225

Rachael Schwartz , Mark Mullery , John Dingliana , Rachel McDonnell

引用次数: 0

Foreword to the special section on 3D object retrieval 2024 symposium (3DOR2024) 三维目标检索2024专题研讨会（3DOR2024）前言

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-09 DOI: 10.1016/j.cag.2025.104235

Benjamin Bustos, Silvia Biasotti, Remco C. Veltkamp, Tobias Schreck, Ivan Sipiran

引用次数: 0

A fast high-dimensional continuation hypercubes algorithm 一种快速高维连续超立方体算法

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-09 DOI: 10.1016/j.cag.2025.104237

Lucas Martinelli Reia , Marcio Gameiro , Tomás Bueno Moraes Ribeiro , Antonio Castelo

引用次数: 0

Persistent interaction: A conceptualization of user-generated artefacts in Visual Analytics 持久交互：可视化分析中用户生成的工件的概念化

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-08 DOI: 10.1016/j.cag.2025.104232

Ignacio Pérez-Messina , Davide Ceneda , Victor Schetinger , Silvia Miksch

引用次数: 0

Computing fast and accurate maps for explaining classification models 计算快速和准确的地图解释分类模型

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-02 DOI: 10.1016/j.cag.2025.104230

Yu Wang, Cristian Grosu, Alexandru Telea

{"title":"Computing fast and accurate maps for explaining classification models","authors":"Yu Wang, Cristian Grosu, Alexandru Telea","doi":"10.1016/j.cag.2025.104230","DOIUrl":"10.1016/j.cag.2025.104230","url":null,"abstract":"<div><div>Image representations of the behavior of trained machine learning classification models can help machine learning engineers examine various aspects of a model such as how it partitions its data space into decision zones separated by decision boundaries; how training samples support the decision in various parts of the data space; and how close training data is to decision boundaries. Yet, for an image of <span><math><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></math></span> pixels, all current methods that create such images have a computational complexity of <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> which precludes their use in interactive visual analytics scenarios. We present a set of techniques for the fast computation of such image-based classifier representations. Compared to earlier work in this area, we accelerate both so-called decision maps, that compute categorical labels, and classifier maps, that compute real-valued quantities, in <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mrow><mo>(</mo><mo>log</mo><mi>n</mi><mo>)</mo></mrow></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> time. Practically, our method has a speed-up of about one order of magnitude and yields results very similar to the ground-truth maps; has no free parameters; is model agnostic; and is simple to implement. We demonstrate our method on several combinations of maps, datasets, and classification models.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"129 ","pages":"Article 104230"},"PeriodicalIF":2.5,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-guided immersive exploration of brain ultrastructure for collaborative analysis and education 人工智能引导下的沉浸式脑超微结构探索，用于协同分析和教育

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-02 DOI: 10.1016/j.cag.2025.104239

Uzair Shah , Marco Agus , Daniya Boges , Hamad Aldous , Vanessa Chiappini , Mahmood Alzubaidi , Markus Hadwiger , Pierre J. Magistretti , Mowafa Househ , Corrado Calí

{"title":"AI-guided immersive exploration of brain ultrastructure for collaborative analysis and education","authors":"Uzair Shah , Marco Agus , Daniya Boges , Hamad Aldous , Vanessa Chiappini , Mahmood Alzubaidi , Markus Hadwiger , Pierre J. Magistretti , Mowafa Househ , Corrado Calí","doi":"10.1016/j.cag.2025.104239","DOIUrl":"10.1016/j.cag.2025.104239","url":null,"abstract":"<div><div>We introduce NeuroVerse, a framework for exploring 3D nanometric-scale reconstructions of neural and glial cellular processes in the central nervous system. Using image stacks from volume electron microscopy, NeuroVerse generates 3D mesh models through a SAM2-based segmentation pipeline and integrates absorption signals for deployment in a Metaverse environment. The framework includes a SAM2 adapter optimized for biological microscopy imaging, adapted with feature enhancement blocks and dual decoders to improve the segmentation of complex cellular structures. An interactive virtual AI agent, powered by Heygen and OpenAI models with domain-specific knowledge, provides semi-real-time assistance. NeuroVerse supports education and collaborative analysis for neuroanatomy and neuroscience. It includes a pipeline for the creation of 3D models, automated segmentation, mesh reconstruction, and heatmap computation, optimized for the Spatial.io ecosystem. Contributions include a virtual anatomy lab for neuroanatomy education and collaborative sessions on spatial morphology correlation and neuroenergetic absorption models. Evaluations show that the SAM2 adapter preserves fine cellular details and manages irregular boundaries. Preliminary sessions indicate potential to enhance neuroscience education, improve remote collaboration among scientists, and provide access to advanced neuroscientific data and tools. Evaluation of the virtual AI agent confirms its ability to provide context-aware support, interpret complex cellular structures, and facilitate understanding through semi-real-time assistance for students analyzing neural and glial reconstructions. NeuroVerse combines imaging, segmentation, and AI technologies within an immersive Metaverse platform for neuroscience education and research.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"129 ","pages":"Article 104239"},"PeriodicalIF":2.5,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial Note for Issue 128 of Computers & Graphics 《计算机与图形学》第128期社论注释

IF 2.5 4区计算机科学

Computers & Graphics-Uk Pub Date : 2025-05-01 DOI: 10.1016/j.cag.2025.104262

Joaquim Jorge (Editor-in-Chief)

引用次数: 0