发电技术 ›› 2021, Vol. 42 ›› Issue (4): 454-463.DOI: 10.12096/j.2096-4528.pgt.21031
收稿日期:
2021-04-25
出版日期:
2021-08-31
发布日期:
2021-07-22
作者简介:
孔祥玲(1984), 女, 博士, 副研究员, 从事机器人运动控制、机器视觉、最优化理论及应用、路径优化控制等方面的研究, kongxiangling@njiet.cn基金资助:
Xiangling KONG1(), Jinglun FU1,2,3,4,5(
)
Received:
2021-04-25
Published:
2021-08-31
Online:
2021-07-22
Supported by:
摘要:
燃气轮机的整机性能及运行安全与其各部件的性能和工作状态密切相关,部件性能优劣和健康状态直接反映在表面的形貌上。因此,根据部件的形貌特征对其性能及健康状态进行判读,是评估燃机设计和健康状态的最为高效的方法,而三维重建技术是实现这一方法的关键技术。首先围绕基于计算机视觉的三维重建,系统地阐述了基于单目视觉、基于双目视觉及基于深度学习的三维重建技术及其发展现状;之后讨论了三维重建技术在燃气轮机行业的发展现状及可能的发展方向;最后,以燃气轮机透平叶片为对象,比较了基于阴影信息及多视图学习网络的三维重建效果并讨论了其各自的优缺点。
中图分类号:
孔祥玲, 付经伦. 基于计算机视觉的三维重建技术在燃气轮机行业的应用及展望[J]. 发电技术, 2021, 42(4): 454-463.
Xiangling KONG, Jinglun FU. Computer-Vision Based on Three-dimensional Reconstruction Technology and Its Applications in Gas Turbine Industry[J]. Power Generation Technology, 2021, 42(4): 454-463.
项目 | 算法名称 | ||
莫尔等高线法 | 相位法 | 傅里叶变换法 | |
光栅类型 | 主光栅、基准光栅 | 正弦光栅 | 罗奇光栅或正弦光栅 |
优点 | 精度高、鲁棒性强 | 精度较高、速度快 | 精度较高、速度快 |
缺点 | 合成大面积莫尔条纹困难、速度慢 | 易受高频噪声和散斑影响而产生误差 | 易受高频噪声和散斑影响而产生误差 |
适用范围 | 小部件的精密测量 | 大部件的快速测量 | 大部件的快速测量 |
表1 基于结构光信息的三维重建算法比较
Tab. 1 Comparison of structured-light based on 3D reconstruction algorithms
项目 | 算法名称 | ||
莫尔等高线法 | 相位法 | 傅里叶变换法 | |
光栅类型 | 主光栅、基准光栅 | 正弦光栅 | 罗奇光栅或正弦光栅 |
优点 | 精度高、鲁棒性强 | 精度较高、速度快 | 精度较高、速度快 |
缺点 | 合成大面积莫尔条纹困难、速度慢 | 易受高频噪声和散斑影响而产生误差 | 易受高频噪声和散斑影响而产生误差 |
适用范围 | 小部件的精密测量 | 大部件的快速测量 | 大部件的快速测量 |
1 | 方继辉, 王荣. 重型F级燃气轮机IGV开度对压气机效率的影响[J]. 发电技术, 2020, 41 (3): 317- 319. |
FANG J H , WANG R . Influence of IGV opening degree on the compressor efficiency of MITSUBISHI F4 gas turbine[J]. Power Generation Technology, 2020, 41 (3): 317- 319. | |
2 | BUTIME J, GUTIERREZ I, CORZO L G, et al. 3D reconstruction methods, a survey[C]//Proceedings of the First International Conference on Computer Vision Theory and Applications, 2015: 457-463. |
3 |
CHEN X , LU C , MA M , et al. Color-coding and phase-shift method for absolute phase measurement[J]. Optics Communications, 2013, 298/299, 54- 58.
DOI |
4 |
TAKEDA M , MUTOH K . Fourier transform profilometry for the automatic measurement of 3-D object shapes[J]. Applied Optics, 1983, 22 (24): 3977- 3982.
DOI |
5 |
FARAJIKHAH S , MADANIPOUR K , SAHARKHIZ S , et al. Shadow moiré aided 3-D reconstruction of fabric drape[J]. Fibers and Polymers, 2012, 13 (7): 928- 935.
DOI |
6 |
IDESAWA M , YATAGAI T , SOMA T . Scanning moiré method and automatic measurement of 3D shapes[J]. Applied Optics, 1977, 16 (8): 2152- 2162.
DOI |
7 |
SRINIVASAN V , LIU H C , HALIOUA M . Automated phase-measuring profilometry of 3-D diffuse objects[J]. Applied Optics, 1984, 23 (18): 3105- 3108.
DOI |
8 |
TAVARES P J , VAZ M A . Linear calibration procedure for the phase-to-height relationship in phase measurement profilometry[J]. Optics Communications, 2007, 274 (2): 307- 314.
DOI |
9 | 沈洋, 陈文静. 抽样对复合傅里叶变换轮廓术的影响[J]. 激光技术, 2008, 32 (1): 80- 83. |
SHEN Y , CHEN E J . Influence of sampling on composite Fourier-transform propfilometry[J]. Laser Technology, 2008, 32 (1): 80- 83. | |
10 | HORN B. Shape from shading: a method for obtaining the shape of a smooth opaque object from one view[D]. Cambridge: Massachusetts Institute of Technology, 1970. |
11 | HORN B . Obtaining shape from shading information[M]. New York: McGraw-Hill, 1989: 123- 171. |
12 | 王学梅. 不同成像条件的从明暗恢复形状算法研究[D]. 长沙: 国防科学技术大学, 2009. |
WANG X M. Research on shape recovery algorithm from light and shade under different imaging conditions[D]. Changsha: National University of Defense Technology, 2009. | |
13 | BROOKS M J, HORN B. Shape and source from shading[R]. Cambridge: Massachusetts Institute of Technology, 1985. |
14 | PARAGIOS N , CHEN Y , FAUGERAS O . Handbook of mathematical models in computer vision[M]. New Nork: Springer, 2006: 108- 112. |
15 | 孙玉娟. 基于光学图像的三维重建理论与技术[M]. 北京: 清华大学出版社, 2017: 125- 126. |
SUN Y J . Theory and technology of 3D reconstruction based on optical image[M]. Beijing: Tsinghua University Press, 2017: 125- 126. | |
16 | ROUY E , TOURIN A . A viscosity solutions approach to shape-from-shading[J]. SIAM Journal on Numerical Analysis, 1992, 867- 884. |
17 |
KIMMEL R , BRUCKSTEIN A M . Tracking level sets by level sets: a method for solving the shape from shading problem[J]. Computer Vision and Image Understanding, 1995, 62 (1): 47- 58.
DOI |
18 |
ZHANG R , TSAI P S , CRYER J E , et al. Shape from shading: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21 (8): 690- 706.
DOI |
19 | PENTLAND A . Shape information from shading: a theory about human perception[J]. Spat Vis, 1989, 4 (2/3): 165- 182. |
20 |
TSAI P , SHAH M . Shape from shading using linear approximation[J]. Image and Vision Computing, 1994, 12 (8): 487- 498.
DOI |
21 |
ZHANG S , NEGAHDARIPOUR S . 3-D shape recovery of planar and curved surfaces from shading cues in underwater images[J]. IEEE Journal of Oceanic Engineering, 2002, 27 (1): 100- 116.
DOI |
22 |
COOPER A P R . A simple shape-from-shading algorithm applied to images of ice-covered terrain[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32 (6): 1196- 1198.
DOI |
23 |
CHO S Y , CHOW T W S . Shape and surface measurement technology by an improved shape-from-shading neural algorithm[J]. IEEE Transactions on Industrial Electronics, 2000, 47 (1): 225- 230.
DOI |
24 | KONG F H. Reconstruction of solder joint surface based on hybrid shape from shading[C]//International Conference on Natural Computation. IEEE Computer Society, 2008: 593-597. |
25 | CASTELAN M, HANCOCK E R. Acquiring height maps of faces from a single image[C]//3D Data Processing, Visualization, and Transmission, International Symposium. IEEE Computer Society. Thessaloniki: IEEE, 2004: 183-190. |
26 |
MCGUNNIGLE G , DONG J . Augmenting photometric stereo with coaxial illumination[J]. IET Computer Vision, 2011, 5 (1): 33- 49.
DOI |
27 | ZHENG Q, CHELLAPPA R. Estimation of illuminant direction, albedo, and shape from shading[C]//IEEE Computer Society Conference on Computer Vision & Pattern Recognition. Maui: IEEE, 2002: 540-545. |
28 | WOODHAM R J . Photometric method for determining surface orientation from multiple images[J]. Optical Engineering, 1992, 19 (1): 151- 171. |
29 | GEORGHIADES A S , BELHUMEUR P N , KRIEGMAN D J . From few to many: illumination cone models for face recognition under variable lighting and pose[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 23 (6): 643- 660. |
30 |
HAYAKAWA H . Photometric stereo under a light source with arbitrary motion[J]. Journal of the Optical Society of America A, 1994, 11 (11): 3079- 3089.
DOI |
31 | PAPADHIMITRI T, FAVARO P. A new perspective on uncalibrated photometric stereo[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Portland: IEEE, 2013: 1474-1481. |
32 |
WOODHAM R J . Gradient and curvature from the photometric-stereo method, including local confidence estimation[J]. Journal of the Optical Society of America A, 1994, 11 (11): 3050- 3068.
DOI |
33 |
COLEMAN E N , JAIN R . Obtaining 3-dimensional shape of textured and specular surfaces using four-source photometry[J]. Computer Graphics and Image Processing, 1982, 18 (4): 309- 328.
DOI |
34 | HERTZMANN A , SEITZ S M . Example-based photometric stereo: shape reconstruction with general, varying BRDFs[J]. IEEE Computer Society, 2005, 27 (8): 1254- 1264. |
35 |
ZHANG Z . A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22 (11): 1330- 1334.
DOI |
36 |
CAI J . Integration of optical flow and dynamic programming for stereo matching[J]. IET Image Processing, 2012, 6 (3): 205- 212.
DOI |
37 | ANSARI M E, MAZOUL A, BENSRHAIR A, et al. A real-time spatio-temporal stereo matching for road applications[C]//International IEEE Conference on Intelligent Transportation Systems. Washington: IEEE, 2011: 1483-1488. |
38 |
STOJAKOVIC V , TEPAVCEVIC B . Image-based modeling approach in creating 3D morphogenetic reconstruction of Liberty Square in Novi Sad[J]. Journal of Cultural Heritage, 2011, 12 (1): 105- 110.
DOI |
39 |
CALAKLI F , TAUBIN G . SSD: smooth signed distance surface reconstruction[J]. Computer Graphics Forum, 2011, 30 (7): 1993- 2002.
DOI |
40 |
MAURICIO K , YUSHI G , TUKI T , et al. Robust 3D image reconstruction of pancreatic cancer tumors from histopathological images with different stains and its quantitative performance evaluation[J]. International Journal of Computer Assisted Radiology and Surgery, 2019, 14 (12): 2047- 2055.
DOI |
41 | SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA, 2015. |
42 | ZHONG Z, JIN L, XIE Z, et al. High performance offline handwritten Chinese character recognition using googlenet and directional feature maps[C]//Proceeding of the 201513th International Conference on Document Analysis and Recognition (ICDAR). Tunis, Tunisia, 2015: 846-850. |
43 | HE K M, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, 2016. |
44 | FAVALLI M , FORNACIAI A , ISOLA I , et al. Multiview 3D reconstruction in geosciences[J]. Computers & Geosciences, 2012, 44, 168- 176. |
45 | FAN H, HAO S, GUIBAS L. A point set generation network for 3D object reconstruction from a single image[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 605-613. |
46 | CHOY C B, XU D, GWAK J, et al. 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction[C]//Proceeding of the European Conference on Computer Vision. 2016. |
47 | PAN J, LI J, HAN X, et al. Residual meshnet: learning to deform meshes for single-view 3D reconstruction[C]//Proceeding of the 2018 International Conference on 3D Vision, 2018. |
48 |
RUAN C , YU T , CHEN F , et al. Experimental characterization of the spatiotemporal dynamics of a turbulent flame in a gas turbine model combustor using computed tomography of chemiluminescence[J]. Energy, 2019, 170, 744- 751.
DOI |
49 | WANG Q , ZHANG Y . Spark characteristics investigation of a gas turbine igniter[J]. Combustion Science and Technology, 2012, 184 (10/12): 1526- 1540. |
50 |
HUANG J , LIU H , WANG Q , et al. Limited-projection volumetric tomography for time-resolved turbulent combustion diagnostics via deep learning[J]. Aerospace Science and Technology, 2020, 106, 106123.
DOI |
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