IEEE Transactions on Visualization and Computer Graphics最新文献_第3页

Towards Quantum Ray Tracing 迈向量子光线追踪

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2024-04-08 DOI: 10.1109/tvcg.2024.3386103

Luís Paulo Santos, Thomas Bashford-Rogers, João Barbosa, Paul Navrátil

引用次数: 0

Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data 工业 X 射线和中子计算机断层扫描数据的双模态可视化

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2024-04-05 DOI: 10.1109/tvcg.2024.3382607

Xuan Huang, Haichao Miao, Andrew Townsend, Kyle Champley, Joseph Tringe, Valerio Pascucci, Peer-Timo Bremer

引用次数: 0

UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene UPST-NeRF：三维场景神经辐射场的通用逼真风格转移

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2024-03-19 DOI: 10.1109/tvcg.2024.3378692

Yaosen Chen, Qi Yuan, Zhiqiang Li, Yuegen Liu, Wei Wang, Chaoping Xie, Xuming Wen, Qien Yu

引用次数: 0

Cartoon Animation Outpainting With Region-Guided Motion Inference 利用区域引导运动推理进行卡通动画外绘

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2024-03-19 DOI: 10.1109/tvcg.2024.3379125

Huisi Wu, Hao Meng, Chengze Li, Xueting Liu, Zhenkun Wen, Tong-Yee Lee

引用次数: 0

A Bio-inspired Model for Bee Simulations 蜜蜂模拟的生物启发模型

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2024-03-19 DOI: 10.1109/tvcg.2024.3379080

Qiang Chen, Wenxiu Guo, Yuming Fang, Yang Tong, Tingsong Lu, Xiaogang Jin, Zhigang Deng

引用次数: 0

A Primal-Dual Box-Constrained QP Pressure Poisson Solver With Topology-Aware Geometry-Inspired Aggregation AMG 具有拓扑感知几何启发聚合 AMG 的原始双箱约束 QP 压力泊松求解器

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2024-03-19 DOI: 10.1109/tvcg.2024.3378725

Tetsuya Takahashi, Christopher Batty

引用次数: 0

SuperUDF: Self-supervised UDF Estimation for Surface Reconstruction SuperUDF:用于表面重建的自监督UDF估计

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-08-28 DOI: 10.48550/arXiv.2308.14371

Hui Tian, Chenyang Zhu, Yifei Shi, Kaiyang Xu

引用次数: 1

SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling SketchMetaFace：一种用于高保真三维人物面部建模的基于学习的绘制界面

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-07-03 DOI: 10.48550/arXiv.2307.00804

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}

引用次数: 0

Neural Projection Mapping Using Reflectance Fields 利用反射场的神经投影映射

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-06-11 DOI: 10.48550/arXiv.2306.06595

Yotam Erel, D. Iwai, Amit H. Bermano

{"title":"Neural Projection Mapping Using Reflectance Fields","authors":"Yotam Erel, D. Iwai, Amit H. Bermano","doi":"10.48550/arXiv.2306.06595","DOIUrl":"https://doi.org/10.48550/arXiv.2306.06595","url":null,"abstract":"We introduce a high resolution spatially adaptive light source, or a projector, into a neural reflectance field that allows to both calibrate the projector and photo realistic light editing. The projected texture is fully differentiable with respect to all scene parameters, and can be optimized to yield a desired appearance suitable for applications in augmented reality and projection mapping. Our neural field consists of three neural networks, estimating geometry, material, and transmittance. Using an analytical BRDF model and carefully selected projection patterns, our acquisition process is simple and intuitive, featuring a fixed uncalibrated projected and a handheld camera with a co-located light source. As we demonstrate, the virtual projector incorporated into the pipeline improves scene understanding and enables various projection mapping applications, alleviating the need for time consuming calibration steps performed in a traditional setting per view or projector location. In addition to enabling novel viewpoint synthesis, we demonstrate state-of-the-art performance projector compensation for novel viewpoints, improvement over the baselines in material and scene reconstruction, and three simply implemented scenarios where projection image optimization is performed, including the use of a 2D generative model to consistently dictate scene appearance from multiple viewpoints. We believe that neural projection mapping opens up the door to novel and exciting downstream tasks, through the joint optimization of the scene and projection images.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43109371","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}

引用次数: 0

DeepTree: Modeling Trees with Situated Latents DeepTree:建模树与定位潜势

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-05-09 DOI: 10.48550/arXiv.2305.05153

Xiaochen Zhou, Bosheng Li, Bedrich Benes, S. Fei, S. Pirk

{"title":"DeepTree: Modeling Trees with Situated Latents","authors":"Xiaochen Zhou, Bosheng Li, Bedrich Benes, S. Fei, S. Pirk","doi":"10.48550/arXiv.2305.05153","DOIUrl":"https://doi.org/10.48550/arXiv.2305.05153","url":null,"abstract":"In this paper, we propose DeepTree, a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them. We call our deep neural model \"situated latent\" because its behavior is determined by the intrinsic state -encoded as a latent space of a deep neural model- and by the extrinsic (environmental) data that is \"situated\" as the location in the 3D space and on the tree structure. We use a neural network pipeline to train a situated latent space that allows us to locally predict branch growth only based on a single node in the branch graph of a tree model. We use this representation to progressively develop new branch nodes, thereby mimicking the growth process of trees. Starting from a root node, a tree is generated by iteratively querying the neural network on the newly added nodes resulting in the branching structure of the whole tree. Our method enables generating a wide variety of tree shapes without the need to define intricate parameters that control their growth and behavior. Furthermore, we show that the situated latents can also be used to encode the environmental response of tree models, e.g., when trees grow next to obstacles. We validate the effectiveness of our method by measuring the similarity of our tree models and by procedurally generated ones based on a number of established metrics for tree form.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47275380","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}

引用次数: 2