Abstract

This paper presents a method to simultaneously measure three-dimensional (3D) surface geometry and temperature in real time. Specifically, we developed 1) a holistic approach to calibrate both a structured light system and a thermal camera under exactly the same world coordinate system even though these two sensors do not share the same wavelength; and 2) a computational framework to determine the sub-pixel corresponding temperature for each 3D point as well as discard those occluded points. Since the thermal 2D imaging and 3D visible imaging systems do not share the same spectrum of light, they can perform sensing simultaneously in real time: we developed a hardware system that can achieve real-time 3D geometry and temperature measurement at 26 Hz with 768 × 960 points per frame.

© 2016 Optical Society of America

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References

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  1. S. Zhang, High-speed 3D Imaging with Digital Fringe Projection Technique (Taylor & Francis (CRC), 2016), 1st ed.
  2. K. Skala, T. Lipić, I. Sović, L. Gjenero, and I. Grubišić, “4d thermal imaging system for medical applications,” Periodicum biologorum 113, 407–416 (2011).
  3. D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
    [Crossref]
  4. J. Rangel, S. Soldan, and A. Kroll, “3D thermal imaging: Fusion of thermography and depth cameras,” International Conference on Quantitative InfraRed Thermography, Bordeaux, France, 2014.
  5. M. A. Akhloufi and B. Verney, “Multimodal registration and fusion for 3d thermal imaging,” Mathematical Problems in Engineering 2015, 450101 (2015).
    [Crossref]
  6. U. R. Dhond and J. K. Aggarwal, “Structure from stereo-a review,” IEEE Trans. Systems, Man. and Cybernetics 19, 1489–1510 (1989).
    [Crossref]
  7. C. P. Keferstein and M. Marxer, “Testing bench for laser triangulation sensors,” Sensor Review 18, 183–187 (1998).
    [Crossref]
  8. A. Kolb, E. Barth, and R. Koch, “Time-of-flight cameras in computer graphics,” Computer Graphics Forum 29, 141–159 (2010).
    [Crossref]
  9. C. Filiberto, C. Roberto, P. Dario, and R. Fulvio, “Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-of-flight camera,” Sensors 9, 10080–10096 (2009).
    [Crossref]
  10. J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Patt. Recogn. 43, 2666–2680 (2010).
    [Crossref]
  11. M. Subbarao and G. Surya, “Depth from defocus: a spatial domain approach,” Int. J. Comput. Vision 13, 271–294 (1994).
    [Crossref]
  12. B. Li, N. Karpinsky, and S. Zhang, “Novel calibration method for structured light system with an out-of-focus projector,” Appl. Opt. 53, 3415–3426 (2014).
    [Crossref] [PubMed]
  13. S. Zhang and P. S. Huang, “Novel method for structured light system calibration,” Opt. Eng. 45, 083601 (2006).
    [Crossref]
  14. S. Coorg and S. Teller, “Real-time occlusion culling for models with large occluders,” in “Proceedings of the 1997 symposium on Interactive 3D graphics” (ACM, 1997), pp. 83-ff.
  15. G. Vaněkčkek, “Back-face culling applied to collision detection of polyhedra,” JOVA 5, 55–63 (1994).
  16. J.-S. Hyun and S. Zhang, “Enhanced two-frequency phase-shifting method,” Appl. Opt. 55, 4395–4401 (2016).
    [Crossref]

2016 (1)

2015 (1)

M. A. Akhloufi and B. Verney, “Multimodal registration and fusion for 3d thermal imaging,” Mathematical Problems in Engineering 2015, 450101 (2015).
[Crossref]

2014 (2)

D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
[Crossref]

B. Li, N. Karpinsky, and S. Zhang, “Novel calibration method for structured light system with an out-of-focus projector,” Appl. Opt. 53, 3415–3426 (2014).
[Crossref] [PubMed]

2011 (1)

K. Skala, T. Lipić, I. Sović, L. Gjenero, and I. Grubišić, “4d thermal imaging system for medical applications,” Periodicum biologorum 113, 407–416 (2011).

2010 (2)

A. Kolb, E. Barth, and R. Koch, “Time-of-flight cameras in computer graphics,” Computer Graphics Forum 29, 141–159 (2010).
[Crossref]

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Patt. Recogn. 43, 2666–2680 (2010).
[Crossref]

2009 (1)

C. Filiberto, C. Roberto, P. Dario, and R. Fulvio, “Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-of-flight camera,” Sensors 9, 10080–10096 (2009).
[Crossref]

2006 (1)

S. Zhang and P. S. Huang, “Novel method for structured light system calibration,” Opt. Eng. 45, 083601 (2006).
[Crossref]

1998 (1)

C. P. Keferstein and M. Marxer, “Testing bench for laser triangulation sensors,” Sensor Review 18, 183–187 (1998).
[Crossref]

1994 (2)

G. Vaněkčkek, “Back-face culling applied to collision detection of polyhedra,” JOVA 5, 55–63 (1994).

M. Subbarao and G. Surya, “Depth from defocus: a spatial domain approach,” Int. J. Comput. Vision 13, 271–294 (1994).
[Crossref]

1989 (1)

U. R. Dhond and J. K. Aggarwal, “Structure from stereo-a review,” IEEE Trans. Systems, Man. and Cybernetics 19, 1489–1510 (1989).
[Crossref]

Aggarwal, J. K.

U. R. Dhond and J. K. Aggarwal, “Structure from stereo-a review,” IEEE Trans. Systems, Man. and Cybernetics 19, 1489–1510 (1989).
[Crossref]

Akhloufi, M. A.

M. A. Akhloufi and B. Verney, “Multimodal registration and fusion for 3d thermal imaging,” Mathematical Problems in Engineering 2015, 450101 (2015).
[Crossref]

Barth, E.

A. Kolb, E. Barth, and R. Koch, “Time-of-flight cameras in computer graphics,” Computer Graphics Forum 29, 141–159 (2010).
[Crossref]

Borrmann, D.

D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
[Crossref]

Coorg, S.

S. Coorg and S. Teller, “Real-time occlusion culling for models with large occluders,” in “Proceedings of the 1997 symposium on Interactive 3D graphics” (ACM, 1997), pp. 83-ff.

Dakulovic, M.

D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
[Crossref]

Dario, P.

C. Filiberto, C. Roberto, P. Dario, and R. Fulvio, “Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-of-flight camera,” Sensors 9, 10080–10096 (2009).
[Crossref]

Dhond, U. R.

U. R. Dhond and J. K. Aggarwal, “Structure from stereo-a review,” IEEE Trans. Systems, Man. and Cybernetics 19, 1489–1510 (1989).
[Crossref]

Fernandez, S.

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Patt. Recogn. 43, 2666–2680 (2010).
[Crossref]

Filiberto, C.

C. Filiberto, C. Roberto, P. Dario, and R. Fulvio, “Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-of-flight camera,” Sensors 9, 10080–10096 (2009).
[Crossref]

Fulvio, R.

C. Filiberto, C. Roberto, P. Dario, and R. Fulvio, “Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-of-flight camera,” Sensors 9, 10080–10096 (2009).
[Crossref]

Gjenero, L.

K. Skala, T. Lipić, I. Sović, L. Gjenero, and I. Grubišić, “4d thermal imaging system for medical applications,” Periodicum biologorum 113, 407–416 (2011).

Grubišic, I.

K. Skala, T. Lipić, I. Sović, L. Gjenero, and I. Grubišić, “4d thermal imaging system for medical applications,” Periodicum biologorum 113, 407–416 (2011).

Huang, P. S.

S. Zhang and P. S. Huang, “Novel method for structured light system calibration,” Opt. Eng. 45, 083601 (2006).
[Crossref]

Hyun, J.-S.

Karpinsky, N.

Keferstein, C. P.

C. P. Keferstein and M. Marxer, “Testing bench for laser triangulation sensors,” Sensor Review 18, 183–187 (1998).
[Crossref]

Koch, R.

A. Kolb, E. Barth, and R. Koch, “Time-of-flight cameras in computer graphics,” Computer Graphics Forum 29, 141–159 (2010).
[Crossref]

Kolb, A.

A. Kolb, E. Barth, and R. Koch, “Time-of-flight cameras in computer graphics,” Computer Graphics Forum 29, 141–159 (2010).
[Crossref]

Kroll, A.

J. Rangel, S. Soldan, and A. Kroll, “3D thermal imaging: Fusion of thermography and depth cameras,” International Conference on Quantitative InfraRed Thermography, Bordeaux, France, 2014.

Li, B.

Lipic, T.

K. Skala, T. Lipić, I. Sović, L. Gjenero, and I. Grubišić, “4d thermal imaging system for medical applications,” Periodicum biologorum 113, 407–416 (2011).

Llado, X.

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Patt. Recogn. 43, 2666–2680 (2010).
[Crossref]

Marxer, M.

C. P. Keferstein and M. Marxer, “Testing bench for laser triangulation sensors,” Sensor Review 18, 183–187 (1998).
[Crossref]

Maurovic, I.

D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
[Crossref]

Nüchter, A.

D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
[Crossref]

Osmankovic, D.

D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
[Crossref]

Petrovic, I.

D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
[Crossref]

Pribanic, T.

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Patt. Recogn. 43, 2666–2680 (2010).
[Crossref]

Rangel, J.

J. Rangel, S. Soldan, and A. Kroll, “3D thermal imaging: Fusion of thermography and depth cameras,” International Conference on Quantitative InfraRed Thermography, Bordeaux, France, 2014.

Roberto, C.

C. Filiberto, C. Roberto, P. Dario, and R. Fulvio, “Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-of-flight camera,” Sensors 9, 10080–10096 (2009).
[Crossref]

Salvi, J.

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Patt. Recogn. 43, 2666–2680 (2010).
[Crossref]

Skala, K.

K. Skala, T. Lipić, I. Sović, L. Gjenero, and I. Grubišić, “4d thermal imaging system for medical applications,” Periodicum biologorum 113, 407–416 (2011).

Soldan, S.

J. Rangel, S. Soldan, and A. Kroll, “3D thermal imaging: Fusion of thermography and depth cameras,” International Conference on Quantitative InfraRed Thermography, Bordeaux, France, 2014.

Sovic, I.

K. Skala, T. Lipić, I. Sović, L. Gjenero, and I. Grubišić, “4d thermal imaging system for medical applications,” Periodicum biologorum 113, 407–416 (2011).

Subbarao, M.

M. Subbarao and G. Surya, “Depth from defocus: a spatial domain approach,” Int. J. Comput. Vision 13, 271–294 (1994).
[Crossref]

Surya, G.

M. Subbarao and G. Surya, “Depth from defocus: a spatial domain approach,” Int. J. Comput. Vision 13, 271–294 (1994).
[Crossref]

Teller, S.

S. Coorg and S. Teller, “Real-time occlusion culling for models with large occluders,” in “Proceedings of the 1997 symposium on Interactive 3D graphics” (ACM, 1997), pp. 83-ff.

Vanekckek, G.

G. Vaněkčkek, “Back-face culling applied to collision detection of polyhedra,” JOVA 5, 55–63 (1994).

Velagic, J.

D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
[Crossref]

Verney, B.

M. A. Akhloufi and B. Verney, “Multimodal registration and fusion for 3d thermal imaging,” Mathematical Problems in Engineering 2015, 450101 (2015).
[Crossref]

Zhang, S.

J.-S. Hyun and S. Zhang, “Enhanced two-frequency phase-shifting method,” Appl. Opt. 55, 4395–4401 (2016).
[Crossref]

B. Li, N. Karpinsky, and S. Zhang, “Novel calibration method for structured light system with an out-of-focus projector,” Appl. Opt. 53, 3415–3426 (2014).
[Crossref] [PubMed]

S. Zhang and P. S. Huang, “Novel method for structured light system calibration,” Opt. Eng. 45, 083601 (2006).
[Crossref]

S. Zhang, High-speed 3D Imaging with Digital Fringe Projection Technique (Taylor & Francis (CRC), 2016), 1st ed.

Adv. Eng. Inform. (1)

D. Borrmann, A. Nüchter, M. Dakulović, I. Maurović, I. Petrović, D. Osmanković, and J. Velagić, “A mobile robot based system for fully automated thermal 3d mapping,” Adv. Eng. Inform. 28, 425–440 (2014).
[Crossref]

Appl. Opt. (2)

Computer Graphics Forum (1)

A. Kolb, E. Barth, and R. Koch, “Time-of-flight cameras in computer graphics,” Computer Graphics Forum 29, 141–159 (2010).
[Crossref]

IEEE Trans. Systems, Man. and Cybernetics (1)

U. R. Dhond and J. K. Aggarwal, “Structure from stereo-a review,” IEEE Trans. Systems, Man. and Cybernetics 19, 1489–1510 (1989).
[Crossref]

Int. J. Comput. Vision (1)

M. Subbarao and G. Surya, “Depth from defocus: a spatial domain approach,” Int. J. Comput. Vision 13, 271–294 (1994).
[Crossref]

JOVA (1)

G. Vaněkčkek, “Back-face culling applied to collision detection of polyhedra,” JOVA 5, 55–63 (1994).

Mathematical Problems in Engineering (1)

M. A. Akhloufi and B. Verney, “Multimodal registration and fusion for 3d thermal imaging,” Mathematical Problems in Engineering 2015, 450101 (2015).
[Crossref]

Opt. Eng. (1)

S. Zhang and P. S. Huang, “Novel method for structured light system calibration,” Opt. Eng. 45, 083601 (2006).
[Crossref]

Patt. Recogn. (1)

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Patt. Recogn. 43, 2666–2680 (2010).
[Crossref]

Periodicum biologorum (1)

K. Skala, T. Lipić, I. Sović, L. Gjenero, and I. Grubišić, “4d thermal imaging system for medical applications,” Periodicum biologorum 113, 407–416 (2011).

Sensor Review (1)

C. P. Keferstein and M. Marxer, “Testing bench for laser triangulation sensors,” Sensor Review 18, 183–187 (1998).
[Crossref]

Sensors (1)

C. Filiberto, C. Roberto, P. Dario, and R. Fulvio, “Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-of-flight camera,” Sensors 9, 10080–10096 (2009).
[Crossref]

Other (3)

S. Zhang, High-speed 3D Imaging with Digital Fringe Projection Technique (Taylor & Francis (CRC), 2016), 1st ed.

J. Rangel, S. Soldan, and A. Kroll, “3D thermal imaging: Fusion of thermography and depth cameras,” International Conference on Quantitative InfraRed Thermography, Bordeaux, France, 2014.

S. Coorg and S. Teller, “Real-time occlusion culling for models with large occluders,” in “Proceedings of the 1997 symposium on Interactive 3D graphics” (ACM, 1997), pp. 83-ff.

Supplementary Material (10)

NameDescription
» Visualization 1: MP4 (259 KB)      Visualization 1
» Visualization 2: MP4 (96 KB)      Visualization 2
» Visualization 3: MP4 (5029 KB)      Visualization 3
» Visualization 4: MP4 (5191 KB)      Visualization 4
» Visualization 5: MP4 (2659 KB)      Visualization 5
» Visualization 6: MP4 (873 KB)      Visualization 6
» Visualization 7: MP4 (132 KB)      Visualization 7
» Visualization 8: MP4 (7782 KB)      Visualization 8
» Visualization 9: MP4 (7593 KB)      Visualization 9
» Visualization 10: MP4 (3220 KB)      Visualization 10

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Figures (9)

Fig. 1
Fig. 1 Stereo calibration between the regular and thermal camera. (a) Calibration board used to calibrate the whole system (image was captured by the regular camera); (b) Image captured by a thermal camera before turning on the heat lamp; (c) System setup to calibrate the thermal and regular camera; (d) Image captured by a thermal camera after turning on the heat lamp.
Fig. 2
Fig. 2 Illustration of culling. Curve A B C ^ can be seen in the view point of Oc, but not the part of Curve D E F ^. Generally speaking, E F ^ can be detected by the occlusion culling algorithm since they are obviously hidden by some other parts, while D E ^ can be detected by the backface culling algorithm since they are on the edge of visible and invisible parts. For better culling results, we combine both occlusion culling and back-face methods.
Fig. 3
Fig. 3 Experimental system setups. (a) Static object measurement system consists of a DLP projector (DELL M115HD), a CMOS camera (Imaging Source 23UX174) and a thermal camera (FLIR A35); (b) Real-time measurement system consists of a thermal camera (FLIR A35), a high-speed DLP projector (LightCrafer 4500), a high-speed CMOS camera (Vision Research Phantom V9.1), and an external timing generator (Arduino UNO R3).
Fig. 4
Fig. 4 Mapping example of a cheeseboard. (a) Image of the cheeseboard captured by the CMOS camera. Its resolution is 1280 × 1024; (b) Image captured by the thermal camera before rectification. Its resolution is 320 × 256;(c) 3D reconstructed geometry; (d) Mapping result. Color represents temperature ranging from 290 to 323 K in both (b) and (d).
Fig. 5
Fig. 5 Zoom-in analysis. (a) Zoomed-in result of the blue rectangle part of Fig. 4(d); (b) Further zoomed-in result of the corner part in (a); (c) Temperature and depth of the cross section in (a). For better comparison, we detrend the depth values using a linear model and shifted them by adding 314 mm. Color represents temperature ranging from 290 to 323 K in (a) and (b).
Fig. 6
Fig. 6 Mapping example of a 3D object. (a) Photography of the measured object; (b) Image captured by the thermal camera before rectification; (c) 3D reconstructed geometry; (d) Temperal mapping result. Color represents temperature ranging from 292 to 297.5 K in both (b) and (d).
Fig. 7
Fig. 7 Zoom-in view of the object showed earlier. (a) The top part of the original 3D geometry; (b) Temperal mapping result; (c) Highlighted points that are culled out as black.
Fig. 8
Fig. 8 Example of real time mapping of hand ( Visualization 1, Visualization 2, Visualization 3, Visualization 4, and Visualization 5 ). (a) Photography of the hand to be measured captured by the CMOS camera (associated with Visualization 1); (b) Image captured by the thermal camera at the same time (associated with Visualization 2); (c) One frame of the 3D reconstructed geometry (associated with Visualization 3); (d) The same frame of the temperature mapping result (associated with Visualization 4). Color represents temperature ranging from 296 to 303 K in both (b) and (d).
Fig. 9
Fig. 9 Example of real time mapping of human face (Visualization 6, Visualization 7, Visualization 8, Visualization 9, and Visualization 10). (a) Photography of the human face captured by the CMOS camera (associated with Visualization 6); (b) Thermal image captured by thermal camera at the same time (associated with Visualization 7); (c) One frame of the 3D reconstructed geometry (associated with Visualization 8); (d) The same frame of temperature mapping result (associated with Visualization 9). Color represents temperature ranging from 297 to 305 K in both (b) and (d).

Equations (19)

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

I k ( x , y ) = I ( x , y ) + I ( x , y ) cos ( ϕ + 2 k π / N ) ,
ϕ = tan 1 [ k = 1 N I k sin ( 2 k π / N ) k = 1 N I k cos ( 2 k π / N ) ] .
s [ u v 1 ] = A [ R , T ] [ x w y w z w 1 ] ,
A = [ f u γ u 0 0 f v v 0 0 0 1 ] , R = [ r 11 r 12 r 13 r 21 r 22 r 23 r 31 r 32 r 33 ] , T = [ t 1 t 2 t 3 ] ,
Dist = [ k 1 , k 2 , p 1 , p 2 , k 3 ]
u = u ( 1 + k 1 r 2 + k 2 r 4 + k 3 r 6 ) , v = v ( 1 + k 1 r 2 + k 2 r 4 + k 3 r 6 ) ,
u = u + [ 2 p 1 u v + p 2 ( r 2 + 2 u 2 ) ] , v = v + [ p 1 ( r 2 + 2 v 2 ) + 2 p 2 u v ] .
s r [ u r v r 1 ] = A r [ R r , T r ] [ x w y w z w 1 ] ,
s p [ u p v p 1 ] = A p [ R p , T p ] [ x w y w z w 1 ] ,
s t [ u t v t 1 ] = A t [ R t , T t ] [ x w y w z w 1 ] ,
f ( i , j ) = exp ( u i j u 0 ) 2 + ( v i j v 0 ) 2 2 σ 2 ,
w ( i , j ) = f ( i , j ) f l o o r ( u 0 ) L + 1 f l o o r ( u 0 ) + L f l o o r ( v 0 ) L + 1 f l o o r ( v 0 ) + L f ( i , j ) ,
T ( u 0 , v 0 ) = f l o o r ( u 0 ) L + 1 f l o o r ( u 0 ) + L f l o o r ( v 0 ) L + 1 f l o o r ( v 0 ) + L w ( i , j ) T ( i , j ) .
S i j = { z p 1 , z p 2 , , z p n i j } ,
z i j min = min { S i j }
z p k > z i j min + t h .
u n e w = f l o o r ( u t × N ) ,
v n e w = f l o o r ( v t × N ) .
n P ( P O c ) > 0 .

Metrics