Abstract

Single vision sensor cannot measure an entire object because of their limited field of view. Meanwhile, multiple rigidly-fixed vision sensors for the dynamic vision measurement of three-dimensional (3D) surface profilometry are complex and sensitive to strong environmental vibrations. To overcome these problems, a novel flexible dynamic measurement method for 3D surface profilometry based on multiple vision sensors is presented in this paper. A raster binocular stereo vision sensor is combined with a wide-field camera to produce a 3D optical probe. Multiple 3D optical probes are arranged around the object being measured, then many planar targets are set up. These planar targets function as the mediator to integrate the local 3D data measured by the raster binocular stereo vision sensors into the coordinate system. The proposed method is not sensitive to strong environmental vibrations, and the positions of these 3D optical probes need not be rigidly-fixed during the measurement. The validity of the proposed method is verified in a physical experiment with two 3D optical probes. When the measuring range of raster binocular stereo vision sensor is about 0.5 m × 0.38 m × 0.4 m and the size of the measured object is about 0.7 m, the accuracy of the proposed method could reach 0.12 mm. Meanwhile, the effectiveness of the proposed method in dynamic measurement is confirmed by measuring the rotating fan blades.

© 2015 Optical Society of America

Full Article  |  PDF Article
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References

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2014 (1)

S. Shirmohammadi and A. Ferrero, “Camera as the instrument: the rising trend of vision based measurement,” IEEE Trans. Instrum. Meas. 17(3), 41–47 (2014).
[Crossref]

2013 (2)

2012 (4)

E. Zappa and G. Busca, “Static and dynamic features of Fourier transform profilometry: A review,” Opt. Lasers Eng. 50(8), 1140–1151 (2012).
[Crossref]

X. Zhang, Y. F. Li, and L. M. Zhu, “Color code identification in coded structured light,” Appl. Opt. 51(22), 5340–5356 (2012).
[Crossref] [PubMed]

S. W. Jung, J. Y. Jeong, and S. J. Ko, “Sharpness Enhancement of Stereo Images Using Binocular Just-Noticeable Difference,” IEEE Trans. Image Process. 21(3), 1191–1199 (2012).
[Crossref] [PubMed]

Q. Li and S. Ren, “A real-Time visual inspection system for discrete surface defects of rail heads,” IEEE Trans. Instrum. Meas. 61(8), 2189–2199 (2012).
[Crossref]

2011 (3)

2010 (4)

Z. G. Ren, J. R. Liao, and L. L. Cai, “Three-dimensional measurement of small mechanical parts under a complicated background based on stereo vision,” Appl. Opt. 49(10), 1789–1801 (2010).
[Crossref] [PubMed]

Y. Li, Y. F. Li, Q. L. Wang, D. Xu, and M. Tan, “Measurement and defect detection of the weld bead based on online vision inspection,” IEEE Trans. Instrum. Meas. 59(7), 1841–1849 (2010).
[Crossref]

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[Crossref]

S. Zhang, “Recent progresses on real-time 3D shape measurement using digital fringe projection techniques,” Opt. Lasers Eng. 48(2), 149–158 (2010).
[Crossref]

2008 (3)

R. Q. Yang, S. Chen, Y. Wei, and Y. Z. Chen, “Robust and accurate surface measurement using structured light,” IEEE Trans. Instrum. Meas. 57(6), 1275–1280 (2008).
[Crossref]

J. Vargas, T. Koninckx, J. A. Quiroga, and L. V. Gool, “Three-dimensional measurement of microchips using structured light techniques,” Opt. Eng. 47(5), 053602 (2008).
[Crossref]

J. Vargas and J. A. Quiroga, “Novel multiresolution approach for an adaptive structured light system,” Opt. Eng. 47(2), 023601 (2008).
[Crossref]

2007 (1)

J. Vargas, M. J. Terrón-López, and J. A. Quiroga, “Flexible calibration procedure for fringe projection profilometry,” Opt. Eng. 46(2), 023601 (2007).
[Crossref]

2003 (2)

E. N. Malamas, E. G. M. Petrakis, M. Zervakis, L. Petit, and J. D. Legat, “A survey on industrial vision systems, applications and tools,” Image Vis. Comput. 21(2), 171–188 (2003).
[Crossref]

P. S. Huang, C. P. Zhang, and F. P. Chiang, “High-speed 3-D shape measurement based on digital fringe projection,” Opt. Eng. 42(1), 163–168 (2003).
[Crossref]

2001 (2)

X. Y. Su, W. J. Chen, Q. C. Zhang, and Y. P. Chao, “Dynamic 3-D shape measurement method based on FTP,” Opt. Lasers Eng. 36(1), 49–64 (2001).

R. S. Lu, Y. F. Li, and Q. Yu, “On-line measurement of straightness of seamless steel pipe using machine vision technique,” Sens. Actuators A Phys. 94(1), 95–101 (2001).
[Crossref]

2000 (2)

Z. Y. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000).
[Crossref]

F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
[Crossref]

1998 (1)

C. Steger, “An unbiased detector of curvilinear structures,” IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 113–125 (1998).
[Crossref]

1992 (1)

P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992).
[Crossref]

Basevi, H. R. A.

Besl, P. J.

P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992).
[Crossref]

Brown, G. M.

F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
[Crossref]

Busca, G.

E. Zappa and G. Busca, “Static and dynamic features of Fourier transform profilometry: A review,” Opt. Lasers Eng. 50(8), 1140–1151 (2012).
[Crossref]

Cai, L. L.

Ceccarelli, M.

M. Ceccarelli, A. Speranza, D. Grimaldi, and F. Lamonaca, “Automatic detection and surface measurements of micronucleus by a computer vision approach,” IEEE Trans. Instrum. Meas. 59(9), 2383–2390 (2010).
[Crossref]

Chao, Y. P.

X. Y. Su, W. J. Chen, Q. C. Zhang, and Y. P. Chao, “Dynamic 3-D shape measurement method based on FTP,” Opt. Lasers Eng. 36(1), 49–64 (2001).

Chen, F.

F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
[Crossref]

Chen, S.

R. Q. Yang, S. Chen, Y. Wei, and Y. Z. Chen, “Robust and accurate surface measurement using structured light,” IEEE Trans. Instrum. Meas. 57(6), 1275–1280 (2008).
[Crossref]

Chen, W. J.

X. Y. Su, W. J. Chen, Q. C. Zhang, and Y. P. Chao, “Dynamic 3-D shape measurement method based on FTP,” Opt. Lasers Eng. 36(1), 49–64 (2001).

Chen, Y. Z.

R. Q. Yang, S. Chen, Y. Wei, and Y. Z. Chen, “Robust and accurate surface measurement using structured light,” IEEE Trans. Instrum. Meas. 57(6), 1275–1280 (2008).
[Crossref]

Chiang, F. P.

P. S. Huang, C. P. Zhang, and F. P. Chiang, “High-speed 3-D shape measurement based on digital fringe projection,” Opt. Eng. 42(1), 163–168 (2003).
[Crossref]

Dehghani, H.

Ferrero, A.

S. Shirmohammadi and A. Ferrero, “Camera as the instrument: the rising trend of vision based measurement,” IEEE Trans. Instrum. Meas. 17(3), 41–47 (2014).
[Crossref]

Fu, Y. J.

Gool, L. V.

J. Vargas, T. Koninckx, J. A. Quiroga, and L. V. Gool, “Three-dimensional measurement of microchips using structured light techniques,” Opt. Eng. 47(5), 053602 (2008).
[Crossref]

Grimaldi, D.

M. Ceccarelli, A. Speranza, D. Grimaldi, and F. Lamonaca, “Automatic detection and surface measurements of micronucleus by a computer vision approach,” IEEE Trans. Instrum. Meas. 59(9), 2383–2390 (2010).
[Crossref]

Guggenheim, J. A.

Guo, Q.

Huang, P. S.

P. S. Huang, C. P. Zhang, and F. P. Chiang, “High-speed 3-D shape measurement based on digital fringe projection,” Opt. Eng. 42(1), 163–168 (2003).
[Crossref]

Jeong, J. Y.

S. W. Jung, J. Y. Jeong, and S. J. Ko, “Sharpness Enhancement of Stereo Images Using Binocular Just-Noticeable Difference,” IEEE Trans. Image Process. 21(3), 1191–1199 (2012).
[Crossref] [PubMed]

Jung, S. W.

S. W. Jung, J. Y. Jeong, and S. J. Ko, “Sharpness Enhancement of Stereo Images Using Binocular Just-Noticeable Difference,” IEEE Trans. Image Process. 21(3), 1191–1199 (2012).
[Crossref] [PubMed]

Ko, S. J.

S. W. Jung, J. Y. Jeong, and S. J. Ko, “Sharpness Enhancement of Stereo Images Using Binocular Just-Noticeable Difference,” IEEE Trans. Image Process. 21(3), 1191–1199 (2012).
[Crossref] [PubMed]

Koninckx, T.

J. Vargas, T. Koninckx, J. A. Quiroga, and L. V. Gool, “Three-dimensional measurement of microchips using structured light techniques,” Opt. Eng. 47(5), 053602 (2008).
[Crossref]

Lamonaca, F.

M. Ceccarelli, A. Speranza, D. Grimaldi, and F. Lamonaca, “Automatic detection and surface measurements of micronucleus by a computer vision approach,” IEEE Trans. Instrum. Meas. 59(9), 2383–2390 (2010).
[Crossref]

Legat, J. D.

E. N. Malamas, E. G. M. Petrakis, M. Zervakis, L. Petit, and J. D. Legat, “A survey on industrial vision systems, applications and tools,” Image Vis. Comput. 21(2), 171–188 (2003).
[Crossref]

Li, Q.

Q. Li and S. Ren, “A real-Time visual inspection system for discrete surface defects of rail heads,” IEEE Trans. Instrum. Meas. 61(8), 2189–2199 (2012).
[Crossref]

Li, W. M.

Li, Y.

Y. Li, Y. F. Li, Q. L. Wang, D. Xu, and M. Tan, “Measurement and defect detection of the weld bead based on online vision inspection,” IEEE Trans. Instrum. Meas. 59(7), 1841–1849 (2010).
[Crossref]

Li, Y. F.

X. Zhang, Y. F. Li, and L. M. Zhu, “Color code identification in coded structured light,” Appl. Opt. 51(22), 5340–5356 (2012).
[Crossref] [PubMed]

W. M. Li and Y. F. Li, “Single-camera panoramic stereo imaging system with a fisheye lens and a convex mirror,” Opt. Express 19(7), 5855–5867 (2011).
[Crossref] [PubMed]

Y. Li, Y. F. Li, Q. L. Wang, D. Xu, and M. Tan, “Measurement and defect detection of the weld bead based on online vision inspection,” IEEE Trans. Instrum. Meas. 59(7), 1841–1849 (2010).
[Crossref]

R. S. Lu, Y. F. Li, and Q. Yu, “On-line measurement of straightness of seamless steel pipe using machine vision technique,” Sens. Actuators A Phys. 94(1), 95–101 (2001).
[Crossref]

Liao, J. R.

Liu, Z.

Z. Liu, G. J. Zhang, Z. Z. Wei, and J. H. Sun, “A global calibration method for multiple vision sensors based on multiple targets,” Meas. Sci. Technol. 22(12), 125102 (2011).
[Crossref]

Lu, L.

Lu, R. S.

R. S. Lu, Y. F. Li, and Q. Yu, “On-line measurement of straightness of seamless steel pipe using machine vision technique,” Sens. Actuators A Phys. 94(1), 95–101 (2001).
[Crossref]

Luo, Q.

Malamas, E. N.

E. N. Malamas, E. G. M. Petrakis, M. Zervakis, L. Petit, and J. D. Legat, “A survey on industrial vision systems, applications and tools,” Image Vis. Comput. 21(2), 171–188 (2003).
[Crossref]

McKay, N. D.

P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992).
[Crossref]

Petit, L.

E. N. Malamas, E. G. M. Petrakis, M. Zervakis, L. Petit, and J. D. Legat, “A survey on industrial vision systems, applications and tools,” Image Vis. Comput. 21(2), 171–188 (2003).
[Crossref]

Petrakis, E. G. M.

E. N. Malamas, E. G. M. Petrakis, M. Zervakis, L. Petit, and J. D. Legat, “A survey on industrial vision systems, applications and tools,” Image Vis. Comput. 21(2), 171–188 (2003).
[Crossref]

Quiroga, J. A.

J. Vargas and J. A. Quiroga, “Novel multiresolution approach for an adaptive structured light system,” Opt. Eng. 47(2), 023601 (2008).
[Crossref]

J. Vargas, T. Koninckx, J. A. Quiroga, and L. V. Gool, “Three-dimensional measurement of microchips using structured light techniques,” Opt. Eng. 47(5), 053602 (2008).
[Crossref]

J. Vargas, M. J. Terrón-López, and J. A. Quiroga, “Flexible calibration procedure for fringe projection profilometry,” Opt. Eng. 46(2), 023601 (2007).
[Crossref]

Ren, S.

Q. Li and S. Ren, “A real-Time visual inspection system for discrete surface defects of rail heads,” IEEE Trans. Instrum. Meas. 61(8), 2189–2199 (2012).
[Crossref]

Ren, Z. G.

Shirmohammadi, S.

S. Shirmohammadi and A. Ferrero, “Camera as the instrument: the rising trend of vision based measurement,” IEEE Trans. Instrum. Meas. 17(3), 41–47 (2014).
[Crossref]

Song, M.

F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
[Crossref]

Speranza, A.

M. Ceccarelli, A. Speranza, D. Grimaldi, and F. Lamonaca, “Automatic detection and surface measurements of micronucleus by a computer vision approach,” IEEE Trans. Instrum. Meas. 59(9), 2383–2390 (2010).
[Crossref]

Steger, C.

C. Steger, “An unbiased detector of curvilinear structures,” IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 113–125 (1998).
[Crossref]

Styles, I. B.

Su, X. Y.

X. Y. Su, W. J. Chen, Q. C. Zhang, and Y. P. Chao, “Dynamic 3-D shape measurement method based on FTP,” Opt. Lasers Eng. 36(1), 49–64 (2001).

Sun, J. H.

Z. Liu, G. J. Zhang, Z. Z. Wei, and J. H. Sun, “A global calibration method for multiple vision sensors based on multiple targets,” Meas. Sci. Technol. 22(12), 125102 (2011).
[Crossref]

Tan, M.

Y. Li, Y. F. Li, Q. L. Wang, D. Xu, and M. Tan, “Measurement and defect detection of the weld bead based on online vision inspection,” IEEE Trans. Instrum. Meas. 59(7), 1841–1849 (2010).
[Crossref]

Terrón-López, M. J.

J. Vargas, M. J. Terrón-López, and J. A. Quiroga, “Flexible calibration procedure for fringe projection profilometry,” Opt. Eng. 46(2), 023601 (2007).
[Crossref]

Vargas, J.

J. Vargas, T. Koninckx, J. A. Quiroga, and L. V. Gool, “Three-dimensional measurement of microchips using structured light techniques,” Opt. Eng. 47(5), 053602 (2008).
[Crossref]

J. Vargas and J. A. Quiroga, “Novel multiresolution approach for an adaptive structured light system,” Opt. Eng. 47(2), 023601 (2008).
[Crossref]

J. Vargas, M. J. Terrón-López, and J. A. Quiroga, “Flexible calibration procedure for fringe projection profilometry,” Opt. Eng. 46(2), 023601 (2007).
[Crossref]

Wang, Q. L.

Y. Li, Y. F. Li, Q. L. Wang, D. Xu, and M. Tan, “Measurement and defect detection of the weld bead based on online vision inspection,” IEEE Trans. Instrum. Meas. 59(7), 1841–1849 (2010).
[Crossref]

Wei, Y.

R. Q. Yang, S. Chen, Y. Wei, and Y. Z. Chen, “Robust and accurate surface measurement using structured light,” IEEE Trans. Instrum. Meas. 57(6), 1275–1280 (2008).
[Crossref]

Wei, Z. Z.

Z. Liu, G. J. Zhang, Z. Z. Wei, and J. H. Sun, “A global calibration method for multiple vision sensors based on multiple targets,” Meas. Sci. Technol. 22(12), 125102 (2011).
[Crossref]

Xi, J. T.

Xu, D.

Y. Li, Y. F. Li, Q. L. Wang, D. Xu, and M. Tan, “Measurement and defect detection of the weld bead based on online vision inspection,” IEEE Trans. Instrum. Meas. 59(7), 1841–1849 (2010).
[Crossref]

Yang, R. Q.

R. Q. Yang, S. Chen, Y. Wei, and Y. Z. Chen, “Robust and accurate surface measurement using structured light,” IEEE Trans. Instrum. Meas. 57(6), 1275–1280 (2008).
[Crossref]

Yu, Q.

R. S. Lu, Y. F. Li, and Q. Yu, “On-line measurement of straightness of seamless steel pipe using machine vision technique,” Sens. Actuators A Phys. 94(1), 95–101 (2001).
[Crossref]

Yu, Y. G.

Zappa, E.

E. Zappa and G. Busca, “Static and dynamic features of Fourier transform profilometry: A review,” Opt. Lasers Eng. 50(8), 1140–1151 (2012).
[Crossref]

Zervakis, M.

E. N. Malamas, E. G. M. Petrakis, M. Zervakis, L. Petit, and J. D. Legat, “A survey on industrial vision systems, applications and tools,” Image Vis. Comput. 21(2), 171–188 (2003).
[Crossref]

Zhang, C. P.

P. S. Huang, C. P. Zhang, and F. P. Chiang, “High-speed 3-D shape measurement based on digital fringe projection,” Opt. Eng. 42(1), 163–168 (2003).
[Crossref]

Zhang, G. J.

Z. Liu, G. J. Zhang, Z. Z. Wei, and J. H. Sun, “A global calibration method for multiple vision sensors based on multiple targets,” Meas. Sci. Technol. 22(12), 125102 (2011).
[Crossref]

Zhang, Q. C.

X. Y. Su, W. J. Chen, Q. C. Zhang, and Y. P. Chao, “Dynamic 3-D shape measurement method based on FTP,” Opt. Lasers Eng. 36(1), 49–64 (2001).

Zhang, S.

S. Zhang, “Recent progresses on real-time 3D shape measurement using digital fringe projection techniques,” Opt. Lasers Eng. 48(2), 149–158 (2010).
[Crossref]

Zhang, X.

Zhang, Z. Y.

Z. Y. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000).
[Crossref]

Zhu, L. M.

Appl. Opt. (2)

IEEE Trans. Image Process. (1)

S. W. Jung, J. Y. Jeong, and S. J. Ko, “Sharpness Enhancement of Stereo Images Using Binocular Just-Noticeable Difference,” IEEE Trans. Image Process. 21(3), 1191–1199 (2012).
[Crossref] [PubMed]

IEEE Trans. Instrum. Meas. (5)

M. Ceccarelli, A. Speranza, D. Grimaldi, and F. Lamonaca, “Automatic detection and surface measurements of micronucleus by a computer vision approach,” IEEE Trans. Instrum. Meas. 59(9), 2383–2390 (2010).
[Crossref]

R. Q. Yang, S. Chen, Y. Wei, and Y. Z. Chen, “Robust and accurate surface measurement using structured light,” IEEE Trans. Instrum. Meas. 57(6), 1275–1280 (2008).
[Crossref]

S. Shirmohammadi and A. Ferrero, “Camera as the instrument: the rising trend of vision based measurement,” IEEE Trans. Instrum. Meas. 17(3), 41–47 (2014).
[Crossref]

Q. Li and S. Ren, “A real-Time visual inspection system for discrete surface defects of rail heads,” IEEE Trans. Instrum. Meas. 61(8), 2189–2199 (2012).
[Crossref]

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Figures (16)

Fig. 1
Fig. 1 Structural schematic of the dynamic vision measuring system for the 3D surface profilometry of a large-scale component under complex site conditions.
Fig. 2
Fig. 2 Structural schematic of the 3D optical probe.
Fig. 3
Fig. 3 Binocular stereo vision model.
Fig. 4
Fig. 4 Structural schematic of the wide-field camera. (a) Sketch map of the wide-field camera with a four-surface mirror. (b) Physical picture of the wide-field camera with a four-surface mirror.
Fig. 5
Fig. 5 Schematic of the global integrated model.
Fig. 6
Fig. 6 Extracting result of the light stripe. (a) Extracting result of the sub-pixel coordinate of the light stripe center. (b) Extracting result of the light stripe center after linking.
Fig. 7
Fig. 7 Schematic of epipolar constraint.
Fig. 8
Fig. 8 Schematic of calibration process of T m4 1 .
Fig. 9
Fig. 9 Layout of physical experiment.
Fig. 10
Fig. 10 Typical images used for the calibration of 3D optical probes.(a) Images captured by the raster binocular stereo vision sensor.(b) and (c) Images captured by the wide-field camera.(d) Images captured by the raster binocular stereo vision sensor and the wide-field camera.
Fig. 11
Fig. 11 (a) Schematic of global measurement accuracy evaluation. (b) Mechanical part
Fig. 12
Fig. 12 Images captured by two probes for the evaluation of global measurement accuracy.(a) Images captured by probe 1.(b) Images captured by probe 2.
Fig. 13
Fig. 13 3D coordinates of all characteristic points transformed to the same coordinate system.
Fig. 14
Fig. 14 Images captured by probe 1 and 2. (a) Images captured by probe 1. (b) Images captured by probe 2.
Fig. 15
Fig. 15 3D surface profilometry of fan blades.(a) 3D surface profilometry of fan blades measured by probe 1.(b) 3D surface profilometry of fan blades measured by probe 1.(c) integration result of (a)and(b).
Fig. 16
Fig. 16 3D surface profilometry of six groups of continuously rotating fan blades.

Tables (3)

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Table 1 Result of parameter calibration of 3D optical probe 1

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Table 2 Result of parameter calibration of 3D optical probe 2

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Table 3 Evaluation result of global measurement accuracy

Equations (8)

Equations on this page are rendered with MathJax. Learn more.

{ ρ 1 p 1 = K 1 [ I 0 ] P s ρ 2 p 2 = K 2 [ R 21 t 21 ] P s
u d = u + ( u u 0 ) ( k 1 r 2 + k 2 r 4 ) v d = v + ( v v 0 ) ( k 1 r 2 + k 2 r 4 )
P o = T so P s
{ P G1 = T N P o1 P G2 = T o 1 , t 1 1 T o 2 , t 1 P o2 P G3 = T o 1 , t3 1 T o 3 , t 3 P o3
H ( u , v ) = [ r u u r u v r u v r v v ]
r u u = g u u ( u , v ) I ( u , v ) r u v = g u v ( u , v ) I ( u , v ) r v v = g v v ( u , v ) I ( u , v )
{ u i ' = u i n u r u + n v r v n u 2 r u u + 2 n u n v r u v + n v 2 r v v n u v i ' = v i n u r u + n v r v n u 2 r u u + 2 n u n v r u v + n v 2 r v v n v
{ ρ p = K 1 ( I 0 ) P a x + b y + c z + d = 0

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