Knowledge Commons of Institute of Automation,CAS
Incremental Poisson Surface Reconstruction for Large Scale Three-Dimensional Modeling | |
Yu, Qiang1,2![]() ![]() ![]() ![]() ![]() | |
2019-11 | |
会议名称 | Chinese Conference on Pattern Recognition and Computer Vision |
会议日期 | 2019-11 |
会议地点 | 陕西省西安市 |
摘要 | A novel Incremental Poisson Surface Reconstruction (IPSR) method based on point clouds and the adaptive octree is proposed in this paper. It solves two problems of the most popular Poisson Surface Reconstruction (PSR) method. First, the PSR is time and memory consuming when treating large scale scenes with millions of points. Second, the PSR can hardly handle the incremental reconstruction for scenes with newly arrived points, unless being restarted on all points. In our method, large scale point clouds are first partitioned into small neighboring blocks. By providing an octree node classification mechanism, the Poisson equation is reformulated with boundary constraints to achieve the seamless reconstruction between adjacent blocks. Solving the Poisson equation with boundary constraints, the indicator function is obtained and the surface mesh is extracted. Experiments on different types of datasets verify the effectiveness and the efficiency of our method. |
关键词 | Surface reconstruction Large scale point cloud Incremental |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 三维视觉 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46624 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 中国科学院自动化研究所 |
通讯作者 | Yu, Qiang |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Yu, Qiang,Sui, Wei,Wang, Ying,et al. Incremental Poisson Surface Reconstruction for Large Scale Three-Dimensional Modeling[C],2019. |
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PRCV2019-papers-part(3665KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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