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  • 汪俊 ( 教授 )

    的个人主页 http://faculty.nuaa.edu.cn/wj8/zh_CN/index.htm

  •   教授   博士生导师
  • 招生学科专业:
    机械工程 -- 【招收硕士研究生】 -- 机电学院
    航空宇航科学与技术 -- 【招收博士、硕士研究生】 -- 机电学院
    机械 -- 【招收博士、硕士研究生】 -- 机电学院
    计算机科学与技术 -- 【招收博士、硕士研究生】 -- 计算机科学与技术学院
论文成果 当前位置: 中文主页 >> 科学研究 >> 论文成果
Constructing 3D CSG Models from 3D Raw Point Clouds

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所属单位:机电学院
发表刊物:COMPUTER GRAPHICS FORUM
关键字:BOUNDARY EVALUATION ALGORITHMS SHAPE CONSTRAINTS CONVERSION
摘要:The Constructive Solid Geometry (CSG) tree, encoding the generative process of an object by a recursive compositional structure of bounded primitives, constitutes an important structural representation of 3D objects. Therefore, automatically recovering such a compositional structure from the raw point cloud of an object represents a high-level reverse engineering problem, finding applications from structure and functionality analysis to creative redesign. We propose an effective method to construct CSG models and trees directly over raw point clouds. Specifically, a large number of hypothetical bounded primitive candidates are first extracted from raw scans, followed by a carefully designed pruning strategy. We then choose to approximate the target CSG model by the combination of a subset of these candidates with corresponding Boolean operations using a binary optimization technique, from which the corresponding CSG tree can be derived. Our method attempts to consider the minimal description length concept in the point cloud analysis setting, where the objective function is designed to minimize the construction error and complexity simultaneously. We demonstrate the effectiveness and robustness of our method with extensive experiments on real scan data with various complexities and styles.
ISSN号:0167-7055
是否译文:否
发表时间:2018-08-01
合写作者:吴巧云,Xu, K.
通讯作者:汪俊

 

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