Blending Surface Segmentation and Editing for 3D Models
文献类型：期刊论文
| 作者 | Zhang, Long2; Guo, Jianwei1,2 ; Xiao, Jun2; Zhang, Xiaopeng1,2; Yan, Dong-Ming1,2
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| 刊名 | IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
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| 出版日期 | 2022-08-01 |
| 卷号 | 28期号:8页码:2879-2894 |
| ISSN号 | 1077-2626 |
| 关键词 | Three-dimensional displays Solid modeling Shape Surface morphology Clustering algorithms Trajectory Partitioning algorithms Mesh segmentation structure recovery superfacets rolling-ball blending surface Markov random field |
| DOI | 10.1109/TVCG.2020.3045450 |
| 通讯作者 | Xiao, Jun(xiaojun@ucas.ac.cn) |
| 英文摘要 | Recognizing and fitting shape primitives from underlying 3D models are key components of many computer graphics and computer vision applications. Although a vast number of structural recovery methods are available, they usually fail to identify blending surfaces, which corresponds to small transitional regions among relatively large primary patches. To address this issue, we present a novel approach for automatic segmentation and surface fitting with accurate geometric parameters from 3D models, especially mechanical parts. Overall, we formulate the structural segmentation as a Markov random field (MRF) labeling problem. In contrast to existing techniques, we first propose a new clustering algorithm to build superfacets by incorporating 3D local geometric information. This algorithm extracts the general quadric and rolling-ball blending regions, and improves the robustness of further segmentation. Next, we apply a specially designed MRF framework to efficiently partition the original model into different meaningful patches of known surface types by defining the multilabel energy function on the superfacets. Furthermore, we present an iterative optimization algorithm based on skeleton extraction to fit rolling-ball blending patches by recovering the parameters of the rolling center trajectories and ball radius. Experiments on different complex models demonstrate the effectiveness and robustness of the proposed method, and the superiority of our method is also verified through comparisons with state-of-the-art approaches. We further apply our algorithm in applications such as mesh editing by changing the radius of the rolling balls. |
| WOS关键词 | MESH SEGMENTATION ; STRUCTURE RECOVERY ; RECONSTRUCTION ; EXTRACTION |
| 资助项目 | National Key RD Program[2018YFB2100602] ; National Natural Science Foundation of China[61802406] ; National Natural Science Foundation of China[61772523] ; National Natural Science Foundation of China[U2003109] ; Beijing Natural Science Foundation[L182059] ; Key Research Program of Frontier Sciences CAS[QYZDY-SSW-SYS004] ; Strategic Priority Research Program of CAS[XDA23090304] ; Youth Innovation Promotion Association of CAS[Y201935] ; Open Project Programof State Key Laboratory of Virtual Reality Technology and Systems Beihang University[VRLAB2019B02] ; Alibaba Group through Alibaba Innovative Research Program ; Fundamental Research Funds for the Central Universities |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | IEEE COMPUTER SOC |
| WOS记录号 | WOS:000819823600006 |
| 资助机构 | National Key RD Program ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Key Research Program of Frontier Sciences CAS ; Strategic Priority Research Program of CAS ; Youth Innovation Promotion Association of CAS ; Open Project Programof State Key Laboratory of Virtual Reality Technology and Systems Beihang University ; Alibaba Group through Alibaba Innovative Research Program ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49173]  |
| 专题 | 模式识别国家重点实验室_三维可视计算 |
| 通讯作者 | Xiao, Jun |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Long,Guo, Jianwei,Xiao, Jun,et al. Blending Surface Segmentation and Editing for 3D Models[J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,2022,28(8):2879-2894. |
| APA | Zhang, Long,Guo, Jianwei,Xiao, Jun,Zhang, Xiaopeng,&Yan, Dong-Ming.(2022).Blending Surface Segmentation and Editing for 3D Models.IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,28(8),2879-2894. |
| MLA | Zhang, Long,et al."Blending Surface Segmentation and Editing for 3D Models".IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 28.8(2022):2879-2894. |
入库方式： OAI收割
来源：自动化研究所
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