Maurice Koch, Nan Cao, Daniel Weiskopf, Kuno Kurzhals
{"title":"Active Gaze Labeling: Visualization for Trust Building","authors":"Maurice Koch, Nan Cao, Daniel Weiskopf, Kuno Kurzhals","doi":"10.1109/tvcg.2024.3392476","DOIUrl":"https://doi.org/10.1109/tvcg.2024.3392476","url":null,"abstract":"","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":"47 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140636662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sangjun Eom, Seijung Kim, Joshua Jackson, David Sykes, Shervin Rahimpour, Maria Gorlatova
{"title":"Augmented Reality-based Contextual Guidance through Surgical Tool Tracking in Neurosurgery","authors":"Sangjun Eom, Seijung Kim, Joshua Jackson, David Sykes, Shervin Rahimpour, Maria Gorlatova","doi":"10.1109/tvcg.2024.3390680","DOIUrl":"https://doi.org/10.1109/tvcg.2024.3390680","url":null,"abstract":"","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":"38 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Every “Body” Gets a Say: An Augmented Optimization Metric to Preserve Body Pose during Avatar Adaptation in Mixed/Augmented Reality","authors":"Alexandra Watkins, Akshith Ullal, Nilanjan Sarkar","doi":"10.1109/tvcg.2024.3388376","DOIUrl":"https://doi.org/10.1109/tvcg.2024.3388376","url":null,"abstract":"","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":"280 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140615963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yiheng Han, Irvin Haozhe Zhan, Long Zeng, Yu-Ping Wang, Ran Yi, Minjing Yu, Matthieu Gaetan Lin, Jenny Sheng, Yong-Jin Liu
{"title":"PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation","authors":"Yiheng Han, Irvin Haozhe Zhan, Long Zeng, Yu-Ping Wang, Ran Yi, Minjing Yu, Matthieu Gaetan Lin, Jenny Sheng, Yong-Jin Liu","doi":"10.1109/tvcg.2024.3390122","DOIUrl":"https://doi.org/10.1109/tvcg.2024.3390122","url":null,"abstract":"","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":"49 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Colormaps for Shaded Surfaces: Stepped vs Smooth","authors":"Colin Ware","doi":"10.1109/tvcg.2024.3383336","DOIUrl":"https://doi.org/10.1109/tvcg.2024.3383336","url":null,"abstract":"","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":"19 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"SuperUDF: Self-supervised UDF Estimation for Surface Reconstruction","authors":"Hui Tian, Chenyang Zhu, Yifei Shi, Kaiyang Xu","doi":"10.48550/arXiv.2308.14371","DOIUrl":"https://doi.org/10.48550/arXiv.2308.14371","url":null,"abstract":"Learning-based surface reconstruction based on unsigned distance functions (UDF) has many advantages such as handling open surfaces. We propose SuperUDF, a self-supervised UDF learning which exploits a learned geometry prior for efficient training and a novel regularization for robustness to sparse sampling. The core idea of SuperUDF draws inspiration from the classical surface approximation operator of locally optimal projection (LOP). The key insight is that if the UDF is estimated correctly, the 3D points should be locally projected onto the underlying surface following the gradient of the UDF. Based on that, a number of inductive biases on UDF geometry and a pre-learned geometry prior are devised to learn UDF estimation efficiently. A novel regularization loss is proposed to make SuperUDF robust to sparse sampling. Furthermore, we also contribute a learning-based mesh extraction from the estimated UDFs. Extensive evaluations demonstrate that SuperUDF outperforms the state of the arts on several public datasets in terms of both quality and efficiency. Code will be released after accteptance.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45000066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhongjin Luo, Dong Du, Heming Zhu, Yizhou Yu, Hongbo Fu, Xiaoguang Han
{"title":"SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling","authors":"Zhongjin Luo, Dong Du, Heming Zhu, Yizhou Yu, Hongbo Fu, Xiaoguang Han","doi":"10.48550/arXiv.2307.00804","DOIUrl":"https://doi.org/10.48550/arXiv.2307.00804","url":null,"abstract":"Modeling 3D avatars benefits various application scenarios such as AR/VR, gaming, and filming. Character faces contribute significant diversity and vividity as a vital component of avatars. However, building 3D character face models usually requires a heavy workload with commercial tools, even for experienced artists. Various existing sketch-based tools fail to support amateurs in modeling diverse facial shapes and rich geometric details. In this paper, we present SketchMetaFace - a sketching system targeting amateur users to model high-fidelity 3D faces in minutes. We carefully design both the user interface and the underlying algorithm. First, curvature-aware strokes are adopted to better support the controllability of carving facial details. Second, considering the key problem of mapping a 2D sketch map to a 3D model, we develop a novel learning-based method termed \"Implicit and Depth Guided Mesh Modeling\" (IDGMM). It fuses the advantages of mesh, implicit, and depth representations to achieve high-quality results with high efficiency. In addition, to further support usability, we present a coarse-to-fine 2D sketching interface design and a data-driven stroke suggestion tool. User studies demonstrate the superiority of our system over existing modeling tools in terms of the ease to use and visual quality of results. Experimental analyses also show that IDGMM reaches a better trade-off between accuracy and efficiency.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48476906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}