Computers & Graphics-Uk最新文献

{"title":"Editorial Note Computers & Graphics Issue 124","authors":"Joaquim Jorge (Editor-in-Chief)","doi":"10.1016/j.cag.2024.104117","DOIUrl":"10.1016/j.cag.2024.104117","url":null,"abstract":"","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"124 ","pages":"Article 104117"},"PeriodicalIF":2.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661622","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}

{"title":"Foreword to the special section on Symposium on Virtual and Augmented Reality 2024 (SVR 2024)","authors":"Rosa Costa, Cléber Corrêa, Skip Rizzo","doi":"10.1016/j.cag.2024.104111","DOIUrl":"10.1016/j.cag.2024.104111","url":null,"abstract":"","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"125 ","pages":"Article 104111"},"PeriodicalIF":2.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652503","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}

{"title":"DVRT: Design and evaluation of a virtual reality drone programming teaching system","authors":"Zean Jin,&nbsp;Yulong Bai,&nbsp;Wei Song,&nbsp;Qinghe Yu,&nbsp;Xiaoxin Yue,&nbsp;Xiang Jia","doi":"10.1016/j.cag.2024.104114","DOIUrl":"10.1016/j.cag.2024.104114","url":null,"abstract":"<div><div>Virtual Reality (VR) is an immersive virtual environment generated through computer technology. VR teaching, by utilizing an immersive learning model, offers innovative learning methods for Science, Technology, Engineering and Mathematics (STEM) education as well as programming education. This study developed a Drone Virtual Reality Teaching (DVRT) system aimed at beginners in drone operation and programming, with the goal of addressing the challenges in traditional drone and programming education, such as difficulty in engaging students and lack of practicality. Through the system's curriculum, students learn basic drone operation skills and advanced programming techniques. We conducted a course experiment primarily targeting undergraduate students who are beginners in drone operation. The test results showed that most students achieved scores above 4 out of 5, indicating that DVRT can effectively promote the development of users' comprehensive STEM literacy and computational thinking, thereby demonstrating the great potential of VR technology in STEM education. Through this innovative teaching method, students not only gain knowledge but also enjoy the fun of immersive learning.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"125 ","pages":"Article 104114"},"PeriodicalIF":2.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594042","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}

{"title":"Fast spline collision detection (FSCD) algorithm for solving multiple contacts in real-time","authors":"Lucas Zanusso Morais ,&nbsp;Marcelo Gomes Martins ,&nbsp;Rafael Piccin Torchelsen ,&nbsp;Anderson Maciel ,&nbsp;Luciana Porcher Nedel","doi":"10.1016/j.cag.2024.104107","DOIUrl":"10.1016/j.cag.2024.104107","url":null,"abstract":"<div><div>Collision detection has been widely studied in the last decades. While plenty of solutions exist, certain simulation scenarios are still challenging when permanent contact and deformable bodies are involved. In this paper, we introduce a novel approach based on volumetric splines that is applicable to complex deformable tubes, such as in the simulation of colonoscopy and other endoscopies. The method relies on modeling radial control points, extracting surface information from a triangle mesh, and storing the volume information around a spline path. Such information is later used to compute the intersection between the object surfaces under the assumption of spatial coherence between neighboring splines. We analyze the method’s performance in terms of both speed and accuracy, comparing it with previous works. Results show that our method solves collisions between complex meshes with over 300k triangles, generating over 1,000 collisions per frame between objects while maintaining an average time of under 1ms without compromising accuracy.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"125 ","pages":"Article 104107"},"PeriodicalIF":2.5,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652502","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}

{"title":"Supporting tailorability in augmented reality based remote assistance in the manufacturing industry: A user study","authors":"Troels Rasmussen ,&nbsp;Kaj Grønbæk ,&nbsp;Weidong Huang","doi":"10.1016/j.cag.2024.104095","DOIUrl":"10.1016/j.cag.2024.104095","url":null,"abstract":"<div><div>Research on remote assistance in real-world industries is sparse, as most research is conducted in the laboratory under controlled conditions. Consequently, little is known about how users tailor remote assistance technologies at work. Therefore, we developed an augmented reality-based remote assistance prototype called Remote Assist Kit (RAK). RAK is a component-based system, allowing us to study tailoring activities and the usefulness of tailorable remote assistance technologies. We conducted a user evaluation with employees from the plastic manufacturing industry. The employees configured the RAK to solve real-world problems in three collaborative scenarios: (1) troubleshooting a running injection molding machine, (2) tool maintenance, (3) solving a trigonometry problem. Our results show that the tailorability of RAK was perceived as useful, and users were able to successfully tailor RAK to the distinct properties of the scenarios. Specific findings and their implications for the design of tailorable remote assistance technologies are presented. Among other findings, requirements specific to remote assistance in the manufacturing industry were discussed, such as the importance of sharing machine sounds between the local operator and the remote helper.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"125 ","pages":"Article 104095"},"PeriodicalIF":2.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating implicit object fragment datasets for machine learning","authors":"Alfonso López ,&nbsp;Antonio J. Rueda ,&nbsp;Rafael J. Segura ,&nbsp;Carlos J. Ogayar ,&nbsp;Pablo Navarro ,&nbsp;José M. Fuertes","doi":"10.1016/j.cag.2024.104104","DOIUrl":"10.1016/j.cag.2024.104104","url":null,"abstract":"<div><div>One of the primary challenges inherent in utilizing deep learning models is the scarcity and accessibility hurdles associated with acquiring datasets of sufficient size to facilitate effective training of these networks. This is particularly significant in object detection, shape completion, and fracture assembly. Instead of scanning a large number of real-world fragments, it is possible to generate massive datasets with synthetic pieces. However, realistic fragmentation is computationally intensive in the preparation (e.g., pre-factured models) and generation. Otherwise, simpler algorithms such as Voronoi diagrams provide faster processing speeds at the expense of compromising realism. In this context, it is required to balance computational efficiency and realism. This paper introduces a GPU-based framework for the massive generation of voxelized fragments derived from high-resolution 3D models, specifically prepared for their utilization as training sets for machine learning models. This rapid pipeline enables controlling how many pieces are produced, their dispersion and the appearance of subtle effects such as erosion. We have tested our pipeline with an archaeological dataset, producing more than 1M fragmented pieces from 1,052 Iberian vessels (<span><span>Github</span><svg><path></path></svg></span>). Although this work primarily intends to provide pieces as implicit data represented by voxels, triangle meshes and point clouds can also be inferred from the initial implicit representation. To underscore the unparalleled benefits of CPU and GPU acceleration in generating vast datasets, we compared against a realistic fragment generator that highlights the potential of our approach, both in terms of applicability and processing time. We also demonstrate the synergies between our pipeline and realistic simulators, which frequently cannot select the number and size of resulting pieces. To this end, a deep learning model was trained over realistic fragments and our dataset, showing similar results.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"125 ","pages":"Article 104104"},"PeriodicalIF":2.5,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ADA-SCMS Net: A self-supervised clustering-based 3D mesh segmentation network with aggregation dual autoencoder","authors":"Xue Jiao ,&nbsp;Xiaohui Yang","doi":"10.1016/j.cag.2024.104100","DOIUrl":"10.1016/j.cag.2024.104100","url":null,"abstract":"<div><div>Despite significant advances in 3D mesh segmentation techniques driven by deep learning, segmenting 3D meshes without exhaustive manual labeling remains a challenging due to difficulties in acquiring high-quality labeled datasets. This paper introduces an <strong>a</strong>ggregation <strong>d</strong>ual <strong>a</strong>utoencoder <strong>s</strong>elf-supervised <strong>c</strong>lustering-based <strong>m</strong>esh <strong>s</strong>egmentation network for unlabeled 3D meshes (ADA-SCMS Net). Expanding upon the previously proposed SCMS-Net, the ADA-SCMS Net enhances the segmentation process by incorporating a denoising autoencoder with an improved graph autoencoder as its basic structure. This modification prompts the segmentation network to concentrate on the primary structure of the input data during training, enabling the capture of robust features. In addition, the ADA-SCMS network introduces two new modules. One module is named the branch aggregation module, which combines the strengths of two branches to create a semantic latent representation. The other is the aggregation self-supervised clustering module, which facilitates end-to-end clustering training by iteratively updating each branch through mutual supervision. Extensive experiments on benchmark datasets validate the effectiveness of the ADA-SCMS network, demonstrating superior segmentation performance compared to the SCMS network.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"124 ","pages":"Article 104100"},"PeriodicalIF":2.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437819","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}

{"title":"Comparative analysis of spatiotemporal playback manipulation on virtual reality training for External Ventricular Drainage","authors":"Andreas Wrife,&nbsp;Renan Guarese,&nbsp;Alessandro Iop,&nbsp;Mario Romero","doi":"10.1016/j.cag.2024.104106","DOIUrl":"10.1016/j.cag.2024.104106","url":null,"abstract":"<div><div>Extensive research has been conducted in multiple surgical specialities where Virtual Reality (VR) has been utilised, such as spinal neurosurgery. However, cranial neurosurgery remains relatively unexplored in this regard. This work explores the impact of adopting VR to study External Ventricular Drainage (EVD). In this study, pre-recorded Motion Captured data of an EVD procedure is visualised on a VR headset, in comparison to a desktop monitor condition. Participants (<span><math><mrow><mi>N</mi><mo>=</mo><mn>20</mn></mrow></math></span>) were tasked with identifying and marking a key moment in the recordings. Objective and subjective metrics were recorded, such as completion time, temporal and spatial error distances, workload, and usability. The results from the experiment showed that the task was completed on average twice as fast in VR, when compared to desktop. However, desktop showed fewer error-prone results. Subjective feedback showed a slightly higher preference towards the VR environment concerning usability, while maintaining a comparable workload. Overall, VR displays are promising as an alternative tool to be used for educational and training purposes in cranial surgery.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"124 ","pages":"Article 104106"},"PeriodicalIF":2.5,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-image SVBRDF estimation with auto-adaptive high-frequency feature extraction","authors":"Jiamin Cheng,&nbsp;Li Wang,&nbsp;Lianghao Zhang,&nbsp;Fangzhou Gao,&nbsp;Jiawan Zhang","doi":"10.1016/j.cag.2024.104103","DOIUrl":"10.1016/j.cag.2024.104103","url":null,"abstract":"<div><div>In this paper, we address the task of estimating spatially-varying bi-directional reflectance distribution functions (SVBRDF) of a near-planar surface from a single flash-lit image. Disentangling SVBRDF from the material appearance by deep learning has proven a formidable challenge. This difficulty is particularly pronounced when dealing with images lit by a point light source because the uneven distribution of irradiance in the scene interacts with the surface, leading to significant global luminance variations across the image. These variations may be overemphasized by the network and wrongly baked into the material property space. To tackle this issue, we propose a high-frequency path that contains an auto-adaptive subband “knob”. This path aims to extract crucial image textures and details while eliminating global luminance variations present in the original image. Furthermore, recognizing that color information is ignored in this path, we design a two-path strategy to jointly estimate material reflectance from both the high-frequency path and the original image. Extensive experiments on a substantial dataset have confirmed the effectiveness of our method. Our method outperforms state-of-the-art methods across a wide range of materials.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"124 ","pages":"Article 104103"},"PeriodicalIF":2.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533416","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}

{"title":"An immersive labeling method for large point clouds","authors":"Tianfang Lin ,&nbsp;Zhongyuan Yu ,&nbsp;Matthew McGinity ,&nbsp;Stefan Gumhold","doi":"10.1016/j.cag.2024.104101","DOIUrl":"10.1016/j.cag.2024.104101","url":null,"abstract":"<div><div>3D point clouds, such as those produced by 3D scanners, often require labeling – the accurate classification of each point into structural or semantic categories – before they can be used in their intended application. However, in the absence of fully automated methods, such labeling must be performed manually, which can prove extremely time and labor intensive. To address this we present a virtual reality tool for accelerating and improving the manual labeling of very large 3D point clouds. The labeling tool provides a variety of 3D interactions for efficient viewing, selection and labeling of points using the controllers of consumer VR-kits. The main contribution of our work is a mixed CPU/GPU-based data structure that supports rendering, selection and labeling with immediate visual feedback at high frame rates necessary for a convenient VR experience. Our mixed CPU/GPU data structure supports fluid interaction with very large point clouds in VR, what is not possible with existing continuous level-of-detail rendering algorithms. We evaluate our method with 25 users on tasks involving point clouds of up to 50 million points and find convincing results that support the case for VR-based point cloud labeling.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"124 ","pages":"Article 104101"},"PeriodicalIF":2.5,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}