Three-dimensional surface reconstruction algorithm based on fusion of scale and confidence

被引：0
|
作者：
Li Y. [2 ]
Lu R. [3 ]
Huang J. [1 ]
Jiang C. [1 ]
Li X. [1 ]
机构：
[1] Microelectronics and Optoelectronics Technology, Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou
[2] Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems, Chenzhou
[3] School of Computer and Artificial Intelligence, Xiangnan University, Chenzhou
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 01期
关键词：
confidence index; continuous implicit function; octree; scale; three-dimensional reconstruction;
D O I：
10.13196/j.cims.2021.0572
中图分类号：
学科分类号：
摘要：
To resolve the problem of improving the three-dimensional surface reconstruction accuracy and completeness for scenes with small structures, a three-dimensional surface reconstruction algorithm based on the fusion of scale and confidence was proposed. A surface implicit function was constructed with Gaussian function as its basis function and third-order Bessel function as its weight function. The coverage of the basis function and the value of the weight function were adjusted adaptively according to the scale and confidence of point clouds. The data was stored and processed in the octree structure, which was adaptively divided according to the scale of point clouds, re-alizing adaptive three-dimensional sampling of the surface implicit function. The three-dimensional surface was obtained as the isosurface of the implicit function through the unconstrained isosurface extraction method. Compared with several typical surface reconstruction algorithms, the experimental results showed that the small structures reconstructed by the proposed algorithm were clear and the overall reconstruction accuracy and completeness were improved. © 2024 CIMS. All rights reserved.
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页码：42 / 52
页数：10
相关论文
共 17 条
  • [1] CHENG Siyuan, YU Guoxin, ZHANG Xlangwei, Surface model reconstruction methodologies in reverse engineering system [J], Computer Integrated Manufacturing Systems, 24, 10, pp. 1934-1939, (2018)
  • [2] LI Xiao, LIU Bin, MEI Xuesong, Et al., Development of an insitu laser machining system using a three-dimensional galvanometer scanner, Engineering, 6, 1, pp. 149-167, (2020)
  • [3] LI Xuebing, HU Tianliang, LIU Zhongqiang, Et al., Optimization method of surface generation based on workpiece features [J], Computer Integrated Manufacturing Systems, 27, 8, pp. 2196-2203, (2021)
  • [4] SHAN Jie, LI Zhixin, ZHANG Wenyuan, Recent progress in large-scale 3D city modeling[J], Acta Geodaetica et Cartography Sinica, 48, 12, pp. 1523-1541, (2019)
  • [5] CURLESS B, LEVOY M., A volumetric method for building complex models from range images, Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 303-312, (1996)
  • [6] KAZHDAN M, BOLITHO M, HOPPE H., Poisson surface reconstruction, Proceedings of the 4th Eurographics Symposium on Geometry Processing, pp. 61-70, (2006)
  • [7] WEN Peizhi, XING Ruhua, WU Xiaojun, Et al., Automatic 3D reconstruction for non-closed surface, Computer Integrated Manufacturing Systems, 19, 4, pp. 680-686, (2013)
  • [8] KAZHDAN M, HOPPE H., Screened poisson surface reconstruction, ACM Transactions on Graphics (ToG), 32, 3, (2013)
  • [9] CALARLI F, TAUBIN G., SSD: Smooth signed distance surface reconstruction[J], Computer Graphics Forum, 30, 7, pp. 1993-2002, (2011)
  • [10] SCHROERS C, SETZER S, WEICKERT J., A variational taxonomy for surface reconstruction from oriented points, Computer Graphics Forum, 33, 5, pp. 195-204, (2014)
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