Abstract

The development of modern infrared applications require simulating thermal representations for targets of interest. However, generating geometric models for simulation has been a laborious, time-consuming work, which greatly limits the practical applications in real-world. In order to reduce the man-in-the-loop requirements, we devise a method that directly and semi-automatically simulates infrared signatures of real urban scenes. From raw meshes generated by multi-view stereo, we automatically produce a simplified watertight model through piecewise-planar 3D reconstruction. Model surface is subdivided into quality mesh elements to attach material attributes. For each element, heat balance equation is solved so as to render the whole scene by synthesizing the radiance distribution in infrared waveband. The credibility and effectiveness of our method are confirmed by comparing simulation results to the measured data in real-world. Our experiments on various types of buildings and large scale scene show that the proposed pipeline simulates meaningful infrared scenes while being robust and scalable.

© 2016 Optical Society of America

Full Article  |  PDF Article
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References

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  1. C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
    [Crossref]
  2. S. E. Paul, “Subpixel temperature estimation from single-band thermal infrared imagery,” Ph.D. thesis, Rochester Institute of Technology (2012).
  3. M. Li, L. Nan, N. Smith, and P. Wonka, “Reconstructing building mass models from UAV images,” Comput. Graph-UK. 54, 84–93 (2016).
    [Crossref]
  4. A. Reinov, Y. Bushlin, A. Lessin, and D. Clement, “Dew, dust, and wind influencing thermal signatures of objects,” Proc. SPIE 6941, 69410U (2008).
    [Crossref]
  5. S. Pallotta, X. Briottet, C. Miesch, and Y. Kerr, “Sensor radiance physical model for rugged heterogeneous surfaces in the 3–14 μm region,” Opt. Express 14(6), 2130–2150 (2006).
    [Crossref] [PubMed]
  6. J. S. Tyo, B. M. Ratliff, J. K. Boger, W. T. Black, D. L. Bowers, and M. P. Fetrow, “The effects of thermal equilibrium and contrast in LWIR polarimetric images,” Opt. Express 15(23), 15161–15167 (2007).
    [Crossref] [PubMed]
  7. H. Ren, R. Liu, G. Yan, ZL. Li, Q. Qin, Q. Liu, and F. Nerry, “Performance evaluation of four directional emissivity analytical models with thermal SAIL model and airborne images,” Opt. Express 23(7), A346–A360 (2015).
    [Crossref] [PubMed]
  8. J. Latger, T. Cathala, N. Douchin, and A. Le Goff, “Simulation of active and passive infrared images using the SE-WORKBENCH,” Proc. SPIE 6543, 654302 (2007).
    [Crossref]
  9. K. Johnson, A. Curran, D. Less, D. Levanen, E. Marttila, T. Gonda, and J. Jones, “MuSES: A new heat and signature management design tool for virtual prototyping,” in Proceedings of the 9th Annual Ground Target Modelling & Validation Conference (Citeseer, 1998).
  10. J. R. Schott, S. D. Brown, R. V. Raqueno, H. N. Gross, and G. Robinson, “An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development,” Can. J. Rem. Sens. 25(2), 99–111 (1999).
    [Crossref]
  11. F. Lafarge and P. Alliez, “Surface reconstruction through point set structuring,” Comput. Graph. Forum 25(2), 225–234 (2013).
    [Crossref]
  12. M. Kazhdan, M. Bolitho, and H. Hoppe, “Poisson surface reconstruction,” in Proceedings of the Fourth Eurographics Symposium on Geometry Processing (Eurographics Association, 2006), 61–70.
  13. A. Hornung and L. Kobbelt, “Robust reconstruction of watertight 3D models from non-uniformly sampled point clouds without normal information,” in Proceedings of Symposium on Geometry Processing (Eurographics Association, 2006), 41–50.
  14. V. Lempitsky and Y. Boykov, “Global optimization for shape fitting,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), 1–8.
  15. P. Labatut, J-P. Patrick, and R. Keriven, “Robust and efficient surface reconstruction from range data,” Comput. Graph. Forum 28(8), 2275–2290 (2009).
    [Crossref]
  16. P. Musialski, P. Wonka, D. Aliaga, M. Wimmer, L. Van Gool, and W. Purgathofer, “A survey of urban reconstruction,” Comput. Graph. Forum 32(6), 146–177 (2013).
    [Crossref]
  17. Q.-Y. Zhou and U. Neumann, “Fast and extensible building modeling from airborne LiDAR data,” in Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM, 2008), 1–8.
  18. C. Poullis and S. You, “Automatic reconstruction of cities from remote sensor data,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), 2775–2782.
  19. H. Yu, E. Li, W. Gong, and S. Han, “Structured image reconstruction for three-dimensional ghost imaging lidar,” Opt. Express 23(11), 14541–14551 (2015).
    [Crossref] [PubMed]
  20. F. Lafarge, “Some new research directions to explore in urban reconstruction,” in Proceedings of Joint Urban Remote Sensing Event (IEEE, 2015), 1–4.
  21. J. M. Coughlan and A.L. Yuille, “The Manhattan world assumption: Regularities in scene statistics which enable Bayesian inference,” in Conference on Neural Information Processing Systems (MIT Press, 2000), 845–851.
  22. C. A. Vanegas, D. G. Aliaga, and B. Benes, “Automatic extraction of Manhattan-world building masses from 3D laser range scans,” IEEE T. Vis. Comput. Gr. 18(10), 1627–1637 (2012).
    [Crossref]
  23. A.-L. Chauve, P. Labatut, and J.-P Pons, “Robust piecewise-planar 3D reconstruction and completion from large-scale unstructured point data,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), 1261–1268.
  24. Q.-Y. Zhou and U. Neumann, “2.5D building modeling by discovering global regularities,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), 326–333.
  25. Y. Verdie, F. Lafarge, and P. Alliez, “LOD Generation for Urban Scenes,” ACM T. Graphic. 34(3), 30 (2015).
    [Crossref]
  26. M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
    [Crossref]
  27. S. P. Sullivan and W. R. Reynolds, “Validation of the physically reasonable infrared signature model (PRISM),” Proc. SPIE 0890, 104–110 (1988).
    [Crossref]
  28. S. E. Lane, C. S. Nichols, A. M. Spencer, and J. M. Cathcart, “OREOS: a new EO-IR modeling and simulation tool for US Coast Guard search and rescue applications,” Proc. SPIE 8713, 87130R (2013).
    [Crossref]
  29. S. R. Lach, S. D. Brown, and J. P. Kerekes, “Semi-automated DIRSIG scene modeling from 3D LiDAR and passive imaging sources,” Proc. SPIE 6214, 62140I (2006).
    [Crossref]
  30. N. Li, Z. Lv, S. Wang, G. Gong, and L. Ren, “A real-time infrared radiation imaging simulation method of aircraft skin with aerodynamic heating effect,” Infrared Phys. Techn. 71, 533–541 (2015).
    [Crossref]
  31. M. Jancosek and T. Pajdla, “Multi-view reconstruction preserving weakly-supported surfaces,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), 3121–3128.
  32. M. Attene, S. Katz, M. Mortara, G. Patané, M. Spagnuolo, and A. Tal, “Mesh segmentation – A comparative study,” in Proceedings of IEEE International Conference on Shape Modeling and Applications (IEEE, 2006), 7.
  33. A. Shamir, “A survey on mesh segmentation techniques,” Comput. Graph. Forum 27(6), 1539–1556 (2008).
    [Crossref]
  34. F. Lafarge, R. Keriven, and M. Brédif, “Combining meshes and geometric primitives for accurate and semantic modeling,” in The British Machine Vision Conference (BMVA, 2009), 38.1–38.11.
  35. D. Cohen-Steiner and J. Morvan, “Restricted Delaunay triangulations and normal cycle,” in Proceedings of the Nineteenth Annual Symposium on Computational Geometry (ACM, 2003), 312–321.
  36. B. Heckel, A. E. Uva, B. Hamann, and K. I. Joy, “Surface reconstruction using adaptive clustering methods,” in Geometric Modelling (Springer, 2001), 199–218.
    [Crossref]
  37. Y. Qin, S. Li, TT. Vu, Z. Niu, and Y. Ban, “Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping,” Opt. Express 23(11), 13761–13775 (2015).
    [Crossref] [PubMed]
  38. Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE T. Pattern Anal. 23(11), 1222–1239 (2001).
    [Crossref]
  39. Y. Li, X. Wu, Y. Chrysathou, A. Sharf, D. Cohen-Or, and N. J. Mitra, “Globfit: Consistently fitting primitives by discovering global relations,” ACM T. Graphic. 30(4), 52 (2011).
    [Crossref]
  40. Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE T. Pattern Anal. 26(9), 1124–1137 (2004).
    [Crossref]
  41. J. Schöberl, “NETGEN An advancing front 2D/3D – mesh generator based on abstract rules,” Comput. Vis. Sci. 1(1), 41–52 (1997).
    [Crossref]
  42. X. Xiong, F. Zhou, X. Bai, and X. Yu, “IR characteristic simulation of city scenes based on radiosity model,” Proc. SPIE 8907, 890727 (2013).
    [Crossref]
  43. B. Bartos and K. Stein, “FTOM-2D: a two-dimensional approach to model the detailed thermal behavior of nonplanar surfaces,” Proc. SPIE 9653, 96530G (2015).
    [Crossref]
  44. Z. Wang, Z. Jiang, S. Liu, and Q. Peng, “New model for realistic IR image rendering of city buildings,” Proc. SPIE 5405, 177–188 (2004).
    [Crossref]
  45. N. Aspert, D. Santa Cruz, and T. Ebrahimi, “MESH: measuring errors between surfaces using the Hausdorff distance,” in Pcoceedings of IEEE International Conference on Multimedia and Expo (IEEE, 2002), 5705–5708.
  46. A. Malaplate, P. Grossmann, and F. Schwenger, “CUBI: a test body for thermal object model validation,” Proc. SPIE 6543, 654305 (2007).
    [Crossref]
  47. MODTRAN, “MODTRAN atmospheric radiative transfer model,” http://www.modtran.org .
  48. T. Foken and C. J. Nappo, Micrometeorology (Springer, 2008), Chap. 4.
  49. F. D. Lapierre and M. Acheroy, “Modelisation of convective heat transfer using novel adaptive parametric models and novel empirical models obtained from measured surface temperatures,” in 5th International IR Target, Background Modelling & Simulation (ITBMS) Workshop (2009).
  50. Solar Air Conditioning Tech Group: solar-ac@yahoogroups.com, “Absorptivity & Emissivity table 1 plus others,” http://www.solarmirror.com/fom/fom-serve/cache/43.html .
  51. J. A. Clarke, P. P. Yaneske, and A. A. Pinney, “The Harmonisation of Thermal Properties of Building Materials,” http://www.esru.strath.ac.uk .

2016 (1)

M. Li, L. Nan, N. Smith, and P. Wonka, “Reconstructing building mass models from UAV images,” Comput. Graph-UK. 54, 84–93 (2016).
[Crossref]

2015 (6)

Y. Verdie, F. Lafarge, and P. Alliez, “LOD Generation for Urban Scenes,” ACM T. Graphic. 34(3), 30 (2015).
[Crossref]

N. Li, Z. Lv, S. Wang, G. Gong, and L. Ren, “A real-time infrared radiation imaging simulation method of aircraft skin with aerodynamic heating effect,” Infrared Phys. Techn. 71, 533–541 (2015).
[Crossref]

B. Bartos and K. Stein, “FTOM-2D: a two-dimensional approach to model the detailed thermal behavior of nonplanar surfaces,” Proc. SPIE 9653, 96530G (2015).
[Crossref]

H. Ren, R. Liu, G. Yan, ZL. Li, Q. Qin, Q. Liu, and F. Nerry, “Performance evaluation of four directional emissivity analytical models with thermal SAIL model and airborne images,” Opt. Express 23(7), A346–A360 (2015).
[Crossref] [PubMed]

Y. Qin, S. Li, TT. Vu, Z. Niu, and Y. Ban, “Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping,” Opt. Express 23(11), 13761–13775 (2015).
[Crossref] [PubMed]

H. Yu, E. Li, W. Gong, and S. Han, “Structured image reconstruction for three-dimensional ghost imaging lidar,” Opt. Express 23(11), 14541–14551 (2015).
[Crossref] [PubMed]

2013 (4)

X. Xiong, F. Zhou, X. Bai, and X. Yu, “IR characteristic simulation of city scenes based on radiosity model,” Proc. SPIE 8907, 890727 (2013).
[Crossref]

S. E. Lane, C. S. Nichols, A. M. Spencer, and J. M. Cathcart, “OREOS: a new EO-IR modeling and simulation tool for US Coast Guard search and rescue applications,” Proc. SPIE 8713, 87130R (2013).
[Crossref]

F. Lafarge and P. Alliez, “Surface reconstruction through point set structuring,” Comput. Graph. Forum 25(2), 225–234 (2013).
[Crossref]

P. Musialski, P. Wonka, D. Aliaga, M. Wimmer, L. Van Gool, and W. Purgathofer, “A survey of urban reconstruction,” Comput. Graph. Forum 32(6), 146–177 (2013).
[Crossref]

2012 (1)

C. A. Vanegas, D. G. Aliaga, and B. Benes, “Automatic extraction of Manhattan-world building masses from 3D laser range scans,” IEEE T. Vis. Comput. Gr. 18(10), 1627–1637 (2012).
[Crossref]

2011 (1)

Y. Li, X. Wu, Y. Chrysathou, A. Sharf, D. Cohen-Or, and N. J. Mitra, “Globfit: Consistently fitting primitives by discovering global relations,” ACM T. Graphic. 30(4), 52 (2011).
[Crossref]

2009 (1)

P. Labatut, J-P. Patrick, and R. Keriven, “Robust and efficient surface reconstruction from range data,” Comput. Graph. Forum 28(8), 2275–2290 (2009).
[Crossref]

2008 (2)

A. Reinov, Y. Bushlin, A. Lessin, and D. Clement, “Dew, dust, and wind influencing thermal signatures of objects,” Proc. SPIE 6941, 69410U (2008).
[Crossref]

A. Shamir, “A survey on mesh segmentation techniques,” Comput. Graph. Forum 27(6), 1539–1556 (2008).
[Crossref]

2007 (3)

J. Latger, T. Cathala, N. Douchin, and A. Le Goff, “Simulation of active and passive infrared images using the SE-WORKBENCH,” Proc. SPIE 6543, 654302 (2007).
[Crossref]

A. Malaplate, P. Grossmann, and F. Schwenger, “CUBI: a test body for thermal object model validation,” Proc. SPIE 6543, 654305 (2007).
[Crossref]

J. S. Tyo, B. M. Ratliff, J. K. Boger, W. T. Black, D. L. Bowers, and M. P. Fetrow, “The effects of thermal equilibrium and contrast in LWIR polarimetric images,” Opt. Express 15(23), 15161–15167 (2007).
[Crossref] [PubMed]

2006 (3)

S. Pallotta, X. Briottet, C. Miesch, and Y. Kerr, “Sensor radiance physical model for rugged heterogeneous surfaces in the 3–14 μm region,” Opt. Express 14(6), 2130–2150 (2006).
[Crossref] [PubMed]

C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
[Crossref]

S. R. Lach, S. D. Brown, and J. P. Kerekes, “Semi-automated DIRSIG scene modeling from 3D LiDAR and passive imaging sources,” Proc. SPIE 6214, 62140I (2006).
[Crossref]

2004 (2)

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE T. Pattern Anal. 26(9), 1124–1137 (2004).
[Crossref]

Z. Wang, Z. Jiang, S. Liu, and Q. Peng, “New model for realistic IR image rendering of city buildings,” Proc. SPIE 5405, 177–188 (2004).
[Crossref]

2001 (1)

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE T. Pattern Anal. 23(11), 1222–1239 (2001).
[Crossref]

1999 (2)

M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
[Crossref]

J. R. Schott, S. D. Brown, R. V. Raqueno, H. N. Gross, and G. Robinson, “An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development,” Can. J. Rem. Sens. 25(2), 99–111 (1999).
[Crossref]

1997 (1)

J. Schöberl, “NETGEN An advancing front 2D/3D – mesh generator based on abstract rules,” Comput. Vis. Sci. 1(1), 41–52 (1997).
[Crossref]

1988 (1)

S. P. Sullivan and W. R. Reynolds, “Validation of the physically reasonable infrared signature model (PRISM),” Proc. SPIE 0890, 104–110 (1988).
[Crossref]

Acheroy, M.

F. D. Lapierre and M. Acheroy, “Modelisation of convective heat transfer using novel adaptive parametric models and novel empirical models obtained from measured surface temperatures,” in 5th International IR Target, Background Modelling & Simulation (ITBMS) Workshop (2009).

Aliaga, D.

P. Musialski, P. Wonka, D. Aliaga, M. Wimmer, L. Van Gool, and W. Purgathofer, “A survey of urban reconstruction,” Comput. Graph. Forum 32(6), 146–177 (2013).
[Crossref]

Aliaga, D. G.

C. A. Vanegas, D. G. Aliaga, and B. Benes, “Automatic extraction of Manhattan-world building masses from 3D laser range scans,” IEEE T. Vis. Comput. Gr. 18(10), 1627–1637 (2012).
[Crossref]

Alliez, P.

Y. Verdie, F. Lafarge, and P. Alliez, “LOD Generation for Urban Scenes,” ACM T. Graphic. 34(3), 30 (2015).
[Crossref]

F. Lafarge and P. Alliez, “Surface reconstruction through point set structuring,” Comput. Graph. Forum 25(2), 225–234 (2013).
[Crossref]

Aspert, N.

N. Aspert, D. Santa Cruz, and T. Ebrahimi, “MESH: measuring errors between surfaces using the Hausdorff distance,” in Pcoceedings of IEEE International Conference on Multimedia and Expo (IEEE, 2002), 5705–5708.

Attene, M.

M. Attene, S. Katz, M. Mortara, G. Patané, M. Spagnuolo, and A. Tal, “Mesh segmentation – A comparative study,” in Proceedings of IEEE International Conference on Shape Modeling and Applications (IEEE, 2006), 7.

Bai, X.

X. Xiong, F. Zhou, X. Bai, and X. Yu, “IR characteristic simulation of city scenes based on radiosity model,” Proc. SPIE 8907, 890727 (2013).
[Crossref]

Ban, Y.

Bartos, B.

B. Bartos and K. Stein, “FTOM-2D: a two-dimensional approach to model the detailed thermal behavior of nonplanar surfaces,” Proc. SPIE 9653, 96530G (2015).
[Crossref]

Benes, B.

C. A. Vanegas, D. G. Aliaga, and B. Benes, “Automatic extraction of Manhattan-world building masses from 3D laser range scans,” IEEE T. Vis. Comput. Gr. 18(10), 1627–1637 (2012).
[Crossref]

Black, W. T.

Boger, J. K.

Bolitho, M.

M. Kazhdan, M. Bolitho, and H. Hoppe, “Poisson surface reconstruction,” in Proceedings of the Fourth Eurographics Symposium on Geometry Processing (Eurographics Association, 2006), 61–70.

Bowers, D. L.

Boykov, Y.

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE T. Pattern Anal. 26(9), 1124–1137 (2004).
[Crossref]

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE T. Pattern Anal. 23(11), 1222–1239 (2001).
[Crossref]

V. Lempitsky and Y. Boykov, “Global optimization for shape fitting,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), 1–8.

Brédif, M.

F. Lafarge, R. Keriven, and M. Brédif, “Combining meshes and geometric primitives for accurate and semantic modeling,” in The British Machine Vision Conference (BMVA, 2009), 38.1–38.11.

Briottet, X.

Brown, S. D.

S. R. Lach, S. D. Brown, and J. P. Kerekes, “Semi-automated DIRSIG scene modeling from 3D LiDAR and passive imaging sources,” Proc. SPIE 6214, 62140I (2006).
[Crossref]

J. R. Schott, S. D. Brown, R. V. Raqueno, H. N. Gross, and G. Robinson, “An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development,” Can. J. Rem. Sens. 25(2), 99–111 (1999).
[Crossref]

Bushlin, Y.

A. Reinov, Y. Bushlin, A. Lessin, and D. Clement, “Dew, dust, and wind influencing thermal signatures of objects,” Proc. SPIE 6941, 69410U (2008).
[Crossref]

Cathala, T.

J. Latger, T. Cathala, N. Douchin, and A. Le Goff, “Simulation of active and passive infrared images using the SE-WORKBENCH,” Proc. SPIE 6543, 654302 (2007).
[Crossref]

Cathcart, J. M.

S. E. Lane, C. S. Nichols, A. M. Spencer, and J. M. Cathcart, “OREOS: a new EO-IR modeling and simulation tool for US Coast Guard search and rescue applications,” Proc. SPIE 8713, 87130R (2013).
[Crossref]

Chauve, A.-L.

A.-L. Chauve, P. Labatut, and J.-P Pons, “Robust piecewise-planar 3D reconstruction and completion from large-scale unstructured point data,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), 1261–1268.

Chrysathou, Y.

Y. Li, X. Wu, Y. Chrysathou, A. Sharf, D. Cohen-Or, and N. J. Mitra, “Globfit: Consistently fitting primitives by discovering global relations,” ACM T. Graphic. 30(4), 52 (2011).
[Crossref]

Clement, D.

A. Reinov, Y. Bushlin, A. Lessin, and D. Clement, “Dew, dust, and wind influencing thermal signatures of objects,” Proc. SPIE 6941, 69410U (2008).
[Crossref]

Cohen-Or, D.

Y. Li, X. Wu, Y. Chrysathou, A. Sharf, D. Cohen-Or, and N. J. Mitra, “Globfit: Consistently fitting primitives by discovering global relations,” ACM T. Graphic. 30(4), 52 (2011).
[Crossref]

Cohen-Steiner, D.

D. Cohen-Steiner and J. Morvan, “Restricted Delaunay triangulations and normal cycle,” in Proceedings of the Nineteenth Annual Symposium on Computational Geometry (ACM, 2003), 312–321.

Coughlan, J. M.

J. M. Coughlan and A.L. Yuille, “The Manhattan world assumption: Regularities in scene statistics which enable Bayesian inference,” in Conference on Neural Information Processing Systems (MIT Press, 2000), 845–851.

Curran, A.

K. Johnson, A. Curran, D. Less, D. Levanen, E. Marttila, T. Gonda, and J. Jones, “MuSES: A new heat and signature management design tool for virtual prototyping,” in Proceedings of the 9th Annual Ground Target Modelling & Validation Conference (Citeseer, 1998).

Douchin, N.

J. Latger, T. Cathala, N. Douchin, and A. Le Goff, “Simulation of active and passive infrared images using the SE-WORKBENCH,” Proc. SPIE 6543, 654302 (2007).
[Crossref]

Ebrahimi, T.

N. Aspert, D. Santa Cruz, and T. Ebrahimi, “MESH: measuring errors between surfaces using the Hausdorff distance,” in Pcoceedings of IEEE International Conference on Multimedia and Expo (IEEE, 2002), 5705–5708.

Fetrow, M. P.

Foken, T.

T. Foken and C. J. Nappo, Micrometeorology (Springer, 2008), Chap. 4.

Foster, J.

M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
[Crossref]

Gonda, T.

K. Johnson, A. Curran, D. Less, D. Levanen, E. Marttila, T. Gonda, and J. Jones, “MuSES: A new heat and signature management design tool for virtual prototyping,” in Proceedings of the 9th Annual Ground Target Modelling & Validation Conference (Citeseer, 1998).

Gong, G.

N. Li, Z. Lv, S. Wang, G. Gong, and L. Ren, “A real-time infrared radiation imaging simulation method of aircraft skin with aerodynamic heating effect,” Infrared Phys. Techn. 71, 533–541 (2015).
[Crossref]

Gong, W.

Gross, H. N.

J. R. Schott, S. D. Brown, R. V. Raqueno, H. N. Gross, and G. Robinson, “An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development,” Can. J. Rem. Sens. 25(2), 99–111 (1999).
[Crossref]

Grossmann, P.

A. Malaplate, P. Grossmann, and F. Schwenger, “CUBI: a test body for thermal object model validation,” Proc. SPIE 6543, 654305 (2007).
[Crossref]

Hamann, B.

B. Heckel, A. E. Uva, B. Hamann, and K. I. Joy, “Surface reconstruction using adaptive clustering methods,” in Geometric Modelling (Springer, 2001), 199–218.
[Crossref]

Han, S.

Heckel, B.

B. Heckel, A. E. Uva, B. Hamann, and K. I. Joy, “Surface reconstruction using adaptive clustering methods,” in Geometric Modelling (Springer, 2001), 199–218.
[Crossref]

Hermansson, P.

C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
[Crossref]

Hoppe, H.

M. Kazhdan, M. Bolitho, and H. Hoppe, “Poisson surface reconstruction,” in Proceedings of the Fourth Eurographics Symposium on Geometry Processing (Eurographics Association, 2006), 61–70.

Hornung, A.

A. Hornung and L. Kobbelt, “Robust reconstruction of watertight 3D models from non-uniformly sampled point clouds without normal information,” in Proceedings of Symposium on Geometry Processing (Eurographics Association, 2006), 41–50.

Jancosek, M.

M. Jancosek and T. Pajdla, “Multi-view reconstruction preserving weakly-supported surfaces,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), 3121–3128.

Jiang, Z.

Z. Wang, Z. Jiang, S. Liu, and Q. Peng, “New model for realistic IR image rendering of city buildings,” Proc. SPIE 5405, 177–188 (2004).
[Crossref]

Johnson, K.

K. Johnson, A. Curran, D. Less, D. Levanen, E. Marttila, T. Gonda, and J. Jones, “MuSES: A new heat and signature management design tool for virtual prototyping,” in Proceedings of the 9th Annual Ground Target Modelling & Validation Conference (Citeseer, 1998).

Jones, J.

K. Johnson, A. Curran, D. Less, D. Levanen, E. Marttila, T. Gonda, and J. Jones, “MuSES: A new heat and signature management design tool for virtual prototyping,” in Proceedings of the 9th Annual Ground Target Modelling & Validation Conference (Citeseer, 1998).

Joy, K. I.

B. Heckel, A. E. Uva, B. Hamann, and K. I. Joy, “Surface reconstruction using adaptive clustering methods,” in Geometric Modelling (Springer, 2001), 199–218.
[Crossref]

Katz, S.

M. Attene, S. Katz, M. Mortara, G. Patané, M. Spagnuolo, and A. Tal, “Mesh segmentation – A comparative study,” in Proceedings of IEEE International Conference on Shape Modeling and Applications (IEEE, 2006), 7.

Kazhdan, M.

M. Kazhdan, M. Bolitho, and H. Hoppe, “Poisson surface reconstruction,” in Proceedings of the Fourth Eurographics Symposium on Geometry Processing (Eurographics Association, 2006), 61–70.

Kerekes, J. P.

S. R. Lach, S. D. Brown, and J. P. Kerekes, “Semi-automated DIRSIG scene modeling from 3D LiDAR and passive imaging sources,” Proc. SPIE 6214, 62140I (2006).
[Crossref]

Keriven, R.

P. Labatut, J-P. Patrick, and R. Keriven, “Robust and efficient surface reconstruction from range data,” Comput. Graph. Forum 28(8), 2275–2290 (2009).
[Crossref]

F. Lafarge, R. Keriven, and M. Brédif, “Combining meshes and geometric primitives for accurate and semantic modeling,” in The British Machine Vision Conference (BMVA, 2009), 38.1–38.11.

Kerr, Y.

Kobbelt, L.

A. Hornung and L. Kobbelt, “Robust reconstruction of watertight 3D models from non-uniformly sampled point clouds without normal information,” in Proceedings of Symposium on Geometry Processing (Eurographics Association, 2006), 41–50.

Kolmogorov, V.

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE T. Pattern Anal. 26(9), 1124–1137 (2004).
[Crossref]

Labatut, P.

P. Labatut, J-P. Patrick, and R. Keriven, “Robust and efficient surface reconstruction from range data,” Comput. Graph. Forum 28(8), 2275–2290 (2009).
[Crossref]

A.-L. Chauve, P. Labatut, and J.-P Pons, “Robust piecewise-planar 3D reconstruction and completion from large-scale unstructured point data,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), 1261–1268.

Lach, S. R.

S. R. Lach, S. D. Brown, and J. P. Kerekes, “Semi-automated DIRSIG scene modeling from 3D LiDAR and passive imaging sources,” Proc. SPIE 6214, 62140I (2006).
[Crossref]

Lafarge, F.

Y. Verdie, F. Lafarge, and P. Alliez, “LOD Generation for Urban Scenes,” ACM T. Graphic. 34(3), 30 (2015).
[Crossref]

F. Lafarge and P. Alliez, “Surface reconstruction through point set structuring,” Comput. Graph. Forum 25(2), 225–234 (2013).
[Crossref]

F. Lafarge, “Some new research directions to explore in urban reconstruction,” in Proceedings of Joint Urban Remote Sensing Event (IEEE, 2015), 1–4.

F. Lafarge, R. Keriven, and M. Brédif, “Combining meshes and geometric primitives for accurate and semantic modeling,” in The British Machine Vision Conference (BMVA, 2009), 38.1–38.11.

Lane, S. E.

S. E. Lane, C. S. Nichols, A. M. Spencer, and J. M. Cathcart, “OREOS: a new EO-IR modeling and simulation tool for US Coast Guard search and rescue applications,” Proc. SPIE 8713, 87130R (2013).
[Crossref]

Lapierre, F. D.

F. D. Lapierre and M. Acheroy, “Modelisation of convective heat transfer using novel adaptive parametric models and novel empirical models obtained from measured surface temperatures,” in 5th International IR Target, Background Modelling & Simulation (ITBMS) Workshop (2009).

Latger, J.

J. Latger, T. Cathala, N. Douchin, and A. Le Goff, “Simulation of active and passive infrared images using the SE-WORKBENCH,” Proc. SPIE 6543, 654302 (2007).
[Crossref]

Le Goff, A.

J. Latger, T. Cathala, N. Douchin, and A. Le Goff, “Simulation of active and passive infrared images using the SE-WORKBENCH,” Proc. SPIE 6543, 654302 (2007).
[Crossref]

Lempitsky, V.

V. Lempitsky and Y. Boykov, “Global optimization for shape fitting,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), 1–8.

Less, D.

K. Johnson, A. Curran, D. Less, D. Levanen, E. Marttila, T. Gonda, and J. Jones, “MuSES: A new heat and signature management design tool for virtual prototyping,” in Proceedings of the 9th Annual Ground Target Modelling & Validation Conference (Citeseer, 1998).

Lessin, A.

A. Reinov, Y. Bushlin, A. Lessin, and D. Clement, “Dew, dust, and wind influencing thermal signatures of objects,” Proc. SPIE 6941, 69410U (2008).
[Crossref]

Levanen, D.

K. Johnson, A. Curran, D. Less, D. Levanen, E. Marttila, T. Gonda, and J. Jones, “MuSES: A new heat and signature management design tool for virtual prototyping,” in Proceedings of the 9th Annual Ground Target Modelling & Validation Conference (Citeseer, 1998).

Li, E.

Li, M.

M. Li, L. Nan, N. Smith, and P. Wonka, “Reconstructing building mass models from UAV images,” Comput. Graph-UK. 54, 84–93 (2016).
[Crossref]

Li, N.

N. Li, Z. Lv, S. Wang, G. Gong, and L. Ren, “A real-time infrared radiation imaging simulation method of aircraft skin with aerodynamic heating effect,” Infrared Phys. Techn. 71, 533–541 (2015).
[Crossref]

Li, S.

Li, Y.

Y. Li, X. Wu, Y. Chrysathou, A. Sharf, D. Cohen-Or, and N. J. Mitra, “Globfit: Consistently fitting primitives by discovering global relations,” ACM T. Graphic. 30(4), 52 (2011).
[Crossref]

Li, ZL.

Liu, Q.

Liu, R.

Liu, S.

Z. Wang, Z. Jiang, S. Liu, and Q. Peng, “New model for realistic IR image rendering of city buildings,” Proc. SPIE 5405, 177–188 (2004).
[Crossref]

Lv, Z.

N. Li, Z. Lv, S. Wang, G. Gong, and L. Ren, “A real-time infrared radiation imaging simulation method of aircraft skin with aerodynamic heating effect,” Infrared Phys. Techn. 71, 533–541 (2015).
[Crossref]

Malaplate, A.

A. Malaplate, P. Grossmann, and F. Schwenger, “CUBI: a test body for thermal object model validation,” Proc. SPIE 6543, 654305 (2007).
[Crossref]

Marttila, E.

K. Johnson, A. Curran, D. Less, D. Levanen, E. Marttila, T. Gonda, and J. Jones, “MuSES: A new heat and signature management design tool for virtual prototyping,” in Proceedings of the 9th Annual Ground Target Modelling & Validation Conference (Citeseer, 1998).

Miesch, C.

Mitra, N. J.

Y. Li, X. Wu, Y. Chrysathou, A. Sharf, D. Cohen-Or, and N. J. Mitra, “Globfit: Consistently fitting primitives by discovering global relations,” ACM T. Graphic. 30(4), 52 (2011).
[Crossref]

Mortara, M.

M. Attene, S. Katz, M. Mortara, G. Patané, M. Spagnuolo, and A. Tal, “Mesh segmentation – A comparative study,” in Proceedings of IEEE International Conference on Shape Modeling and Applications (IEEE, 2006), 7.

Morvan, J.

D. Cohen-Steiner and J. Morvan, “Restricted Delaunay triangulations and normal cycle,” in Proceedings of the Nineteenth Annual Symposium on Computational Geometry (ACM, 2003), 312–321.

Musialski, P.

P. Musialski, P. Wonka, D. Aliaga, M. Wimmer, L. Van Gool, and W. Purgathofer, “A survey of urban reconstruction,” Comput. Graph. Forum 32(6), 146–177 (2013).
[Crossref]

Nan, L.

M. Li, L. Nan, N. Smith, and P. Wonka, “Reconstructing building mass models from UAV images,” Comput. Graph-UK. 54, 84–93 (2016).
[Crossref]

Nappo, C. J.

T. Foken and C. J. Nappo, Micrometeorology (Springer, 2008), Chap. 4.

Nelsson, C.

C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
[Crossref]

Nerry, F.

Neumann, U.

Q.-Y. Zhou and U. Neumann, “Fast and extensible building modeling from airborne LiDAR data,” in Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM, 2008), 1–8.

Q.-Y. Zhou and U. Neumann, “2.5D building modeling by discovering global regularities,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), 326–333.

Nichols, C. S.

S. E. Lane, C. S. Nichols, A. M. Spencer, and J. M. Cathcart, “OREOS: a new EO-IR modeling and simulation tool for US Coast Guard search and rescue applications,” Proc. SPIE 8713, 87130R (2013).
[Crossref]

Niu, Z.

Nyberg, S.

C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
[Crossref]

Owens, M. A.

M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
[Crossref]

Pajdla, T.

M. Jancosek and T. Pajdla, “Multi-view reconstruction preserving weakly-supported surfaces,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), 3121–3128.

Pallotta, S.

Patané, G.

M. Attene, S. Katz, M. Mortara, G. Patané, M. Spagnuolo, and A. Tal, “Mesh segmentation – A comparative study,” in Proceedings of IEEE International Conference on Shape Modeling and Applications (IEEE, 2006), 7.

Patrick, J-P.

P. Labatut, J-P. Patrick, and R. Keriven, “Robust and efficient surface reconstruction from range data,” Comput. Graph. Forum 28(8), 2275–2290 (2009).
[Crossref]

Paul, S. E.

S. E. Paul, “Subpixel temperature estimation from single-band thermal infrared imagery,” Ph.D. thesis, Rochester Institute of Technology (2012).

Peng, Q.

Z. Wang, Z. Jiang, S. Liu, and Q. Peng, “New model for realistic IR image rendering of city buildings,” Proc. SPIE 5405, 177–188 (2004).
[Crossref]

Persson, A.

C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
[Crossref]

Persson, R.

C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
[Crossref]

Pons, J.-P

A.-L. Chauve, P. Labatut, and J.-P Pons, “Robust piecewise-planar 3D reconstruction and completion from large-scale unstructured point data,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), 1261–1268.

Poullis, C.

C. Poullis and S. You, “Automatic reconstruction of cities from remote sensor data,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), 2775–2782.

Purgathofer, W.

P. Musialski, P. Wonka, D. Aliaga, M. Wimmer, L. Van Gool, and W. Purgathofer, “A survey of urban reconstruction,” Comput. Graph. Forum 32(6), 146–177 (2013).
[Crossref]

Qin, Q.

Qin, Y.

Raqueno, R. V.

J. R. Schott, S. D. Brown, R. V. Raqueno, H. N. Gross, and G. Robinson, “An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development,” Can. J. Rem. Sens. 25(2), 99–111 (1999).
[Crossref]

Ratliff, B. M.

Reinov, A.

A. Reinov, Y. Bushlin, A. Lessin, and D. Clement, “Dew, dust, and wind influencing thermal signatures of objects,” Proc. SPIE 6941, 69410U (2008).
[Crossref]

Ren, H.

Ren, L.

N. Li, Z. Lv, S. Wang, G. Gong, and L. Ren, “A real-time infrared radiation imaging simulation method of aircraft skin with aerodynamic heating effect,” Infrared Phys. Techn. 71, 533–541 (2015).
[Crossref]

Reynolds, W. R.

S. P. Sullivan and W. R. Reynolds, “Validation of the physically reasonable infrared signature model (PRISM),” Proc. SPIE 0890, 104–110 (1988).
[Crossref]

Richards, M.

M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
[Crossref]

Robinson, G.

J. R. Schott, S. D. Brown, R. V. Raqueno, H. N. Gross, and G. Robinson, “An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development,” Can. J. Rem. Sens. 25(2), 99–111 (1999).
[Crossref]

Santa Cruz, D.

N. Aspert, D. Santa Cruz, and T. Ebrahimi, “MESH: measuring errors between surfaces using the Hausdorff distance,” in Pcoceedings of IEEE International Conference on Multimedia and Expo (IEEE, 2002), 5705–5708.

Schöberl, J.

J. Schöberl, “NETGEN An advancing front 2D/3D – mesh generator based on abstract rules,” Comput. Vis. Sci. 1(1), 41–52 (1997).
[Crossref]

Schott, J. R.

J. R. Schott, S. D. Brown, R. V. Raqueno, H. N. Gross, and G. Robinson, “An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development,” Can. J. Rem. Sens. 25(2), 99–111 (1999).
[Crossref]

Schwenger, F.

A. Malaplate, P. Grossmann, and F. Schwenger, “CUBI: a test body for thermal object model validation,” Proc. SPIE 6543, 654305 (2007).
[Crossref]

Shamir, A.

A. Shamir, “A survey on mesh segmentation techniques,” Comput. Graph. Forum 27(6), 1539–1556 (2008).
[Crossref]

Sharf, A.

Y. Li, X. Wu, Y. Chrysathou, A. Sharf, D. Cohen-Or, and N. J. Mitra, “Globfit: Consistently fitting primitives by discovering global relations,” ACM T. Graphic. 30(4), 52 (2011).
[Crossref]

Sjökvist, S.

C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
[Crossref]

Smith, N.

M. Li, L. Nan, N. Smith, and P. Wonka, “Reconstructing building mass models from UAV images,” Comput. Graph-UK. 54, 84–93 (2016).
[Crossref]

Spagnuolo, M.

M. Attene, S. Katz, M. Mortara, G. Patané, M. Spagnuolo, and A. Tal, “Mesh segmentation – A comparative study,” in Proceedings of IEEE International Conference on Shape Modeling and Applications (IEEE, 2006), 7.

Spencer, A. M.

S. E. Lane, C. S. Nichols, A. M. Spencer, and J. M. Cathcart, “OREOS: a new EO-IR modeling and simulation tool for US Coast Guard search and rescue applications,” Proc. SPIE 8713, 87130R (2013).
[Crossref]

Stein, K.

B. Bartos and K. Stein, “FTOM-2D: a two-dimensional approach to model the detailed thermal behavior of nonplanar surfaces,” Proc. SPIE 9653, 96530G (2015).
[Crossref]

Sullivan, S. P.

S. P. Sullivan and W. R. Reynolds, “Validation of the physically reasonable infrared signature model (PRISM),” Proc. SPIE 0890, 104–110 (1988).
[Crossref]

Tal, A.

M. Attene, S. Katz, M. Mortara, G. Patané, M. Spagnuolo, and A. Tal, “Mesh segmentation – A comparative study,” in Proceedings of IEEE International Conference on Shape Modeling and Applications (IEEE, 2006), 7.

Tyo, J. S.

Underwood, V.

M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
[Crossref]

Uva, A. E.

B. Heckel, A. E. Uva, B. Hamann, and K. I. Joy, “Surface reconstruction using adaptive clustering methods,” in Geometric Modelling (Springer, 2001), 199–218.
[Crossref]

Van Gool, L.

P. Musialski, P. Wonka, D. Aliaga, M. Wimmer, L. Van Gool, and W. Purgathofer, “A survey of urban reconstruction,” Comput. Graph. Forum 32(6), 146–177 (2013).
[Crossref]

Vanegas, C. A.

C. A. Vanegas, D. G. Aliaga, and B. Benes, “Automatic extraction of Manhattan-world building masses from 3D laser range scans,” IEEE T. Vis. Comput. Gr. 18(10), 1627–1637 (2012).
[Crossref]

Vechinski, D. A.

M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
[Crossref]

Veksler, O.

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE T. Pattern Anal. 23(11), 1222–1239 (2001).
[Crossref]

Verdie, Y.

Y. Verdie, F. Lafarge, and P. Alliez, “LOD Generation for Urban Scenes,” ACM T. Graphic. 34(3), 30 (2015).
[Crossref]

Vu, TT.

Wang, S.

N. Li, Z. Lv, S. Wang, G. Gong, and L. Ren, “A real-time infrared radiation imaging simulation method of aircraft skin with aerodynamic heating effect,” Infrared Phys. Techn. 71, 533–541 (2015).
[Crossref]

Wang, Z.

Z. Wang, Z. Jiang, S. Liu, and Q. Peng, “New model for realistic IR image rendering of city buildings,” Proc. SPIE 5405, 177–188 (2004).
[Crossref]

Watson, J. S.

M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
[Crossref]

Wellfare, M. R.

M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
[Crossref]

Wimmer, M.

P. Musialski, P. Wonka, D. Aliaga, M. Wimmer, L. Van Gool, and W. Purgathofer, “A survey of urban reconstruction,” Comput. Graph. Forum 32(6), 146–177 (2013).
[Crossref]

Winzell, T.

C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
[Crossref]

Wonka, P.

M. Li, L. Nan, N. Smith, and P. Wonka, “Reconstructing building mass models from UAV images,” Comput. Graph-UK. 54, 84–93 (2016).
[Crossref]

P. Musialski, P. Wonka, D. Aliaga, M. Wimmer, L. Van Gool, and W. Purgathofer, “A survey of urban reconstruction,” Comput. Graph. Forum 32(6), 146–177 (2013).
[Crossref]

Wu, X.

Y. Li, X. Wu, Y. Chrysathou, A. Sharf, D. Cohen-Or, and N. J. Mitra, “Globfit: Consistently fitting primitives by discovering global relations,” ACM T. Graphic. 30(4), 52 (2011).
[Crossref]

Xiong, X.

X. Xiong, F. Zhou, X. Bai, and X. Yu, “IR characteristic simulation of city scenes based on radiosity model,” Proc. SPIE 8907, 890727 (2013).
[Crossref]

Yan, G.

You, S.

C. Poullis and S. You, “Automatic reconstruction of cities from remote sensor data,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), 2775–2782.

Yu, H.

Yu, X.

X. Xiong, F. Zhou, X. Bai, and X. Yu, “IR characteristic simulation of city scenes based on radiosity model,” Proc. SPIE 8907, 890727 (2013).
[Crossref]

Yuille, A.L.

J. M. Coughlan and A.L. Yuille, “The Manhattan world assumption: Regularities in scene statistics which enable Bayesian inference,” in Conference on Neural Information Processing Systems (MIT Press, 2000), 845–851.

Zabih, R.

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE T. Pattern Anal. 23(11), 1222–1239 (2001).
[Crossref]

Zhou, F.

X. Xiong, F. Zhou, X. Bai, and X. Yu, “IR characteristic simulation of city scenes based on radiosity model,” Proc. SPIE 8907, 890727 (2013).
[Crossref]

Zhou, Q.-Y.

Q.-Y. Zhou and U. Neumann, “Fast and extensible building modeling from airborne LiDAR data,” in Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM, 2008), 1–8.

Q.-Y. Zhou and U. Neumann, “2.5D building modeling by discovering global regularities,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), 326–333.

ACM T. Graphic. (2)

Y. Verdie, F. Lafarge, and P. Alliez, “LOD Generation for Urban Scenes,” ACM T. Graphic. 34(3), 30 (2015).
[Crossref]

Y. Li, X. Wu, Y. Chrysathou, A. Sharf, D. Cohen-Or, and N. J. Mitra, “Globfit: Consistently fitting primitives by discovering global relations,” ACM T. Graphic. 30(4), 52 (2011).
[Crossref]

Can. J. Rem. Sens. (1)

J. R. Schott, S. D. Brown, R. V. Raqueno, H. N. Gross, and G. Robinson, “An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development,” Can. J. Rem. Sens. 25(2), 99–111 (1999).
[Crossref]

Comput. Graph-UK. (1)

M. Li, L. Nan, N. Smith, and P. Wonka, “Reconstructing building mass models from UAV images,” Comput. Graph-UK. 54, 84–93 (2016).
[Crossref]

Comput. Graph. Forum (4)

F. Lafarge and P. Alliez, “Surface reconstruction through point set structuring,” Comput. Graph. Forum 25(2), 225–234 (2013).
[Crossref]

P. Labatut, J-P. Patrick, and R. Keriven, “Robust and efficient surface reconstruction from range data,” Comput. Graph. Forum 28(8), 2275–2290 (2009).
[Crossref]

P. Musialski, P. Wonka, D. Aliaga, M. Wimmer, L. Van Gool, and W. Purgathofer, “A survey of urban reconstruction,” Comput. Graph. Forum 32(6), 146–177 (2013).
[Crossref]

A. Shamir, “A survey on mesh segmentation techniques,” Comput. Graph. Forum 27(6), 1539–1556 (2008).
[Crossref]

Comput. Vis. Sci. (1)

J. Schöberl, “NETGEN An advancing front 2D/3D – mesh generator based on abstract rules,” Comput. Vis. Sci. 1(1), 41–52 (1997).
[Crossref]

IEEE T. Pattern Anal. (2)

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE T. Pattern Anal. 23(11), 1222–1239 (2001).
[Crossref]

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE T. Pattern Anal. 26(9), 1124–1137 (2004).
[Crossref]

IEEE T. Vis. Comput. Gr. (1)

C. A. Vanegas, D. G. Aliaga, and B. Benes, “Automatic extraction of Manhattan-world building masses from 3D laser range scans,” IEEE T. Vis. Comput. Gr. 18(10), 1627–1637 (2012).
[Crossref]

Infrared Phys. Techn. (1)

N. Li, Z. Lv, S. Wang, G. Gong, and L. Ren, “A real-time infrared radiation imaging simulation method of aircraft skin with aerodynamic heating effect,” Infrared Phys. Techn. 71, 533–541 (2015).
[Crossref]

Opt. Express (5)

Proc. SPIE (11)

C. Nelsson, P. Hermansson, S. Nyberg, A. Persson, R. Persson, S. Sjökvist, and T. Winzell, “Optical signature modeling at FOI,” Proc. SPIE 6395, 639508 (2006).
[Crossref]

A. Reinov, Y. Bushlin, A. Lessin, and D. Clement, “Dew, dust, and wind influencing thermal signatures of objects,” Proc. SPIE 6941, 69410U (2008).
[Crossref]

J. Latger, T. Cathala, N. Douchin, and A. Le Goff, “Simulation of active and passive infrared images using the SE-WORKBENCH,” Proc. SPIE 6543, 654302 (2007).
[Crossref]

M. A. Owens, M. R. Wellfare, J. Foster, J. S. Watson, D. A. Vechinski, M. Richards, and V. Underwood, “IRMA 5.0 MultiSensor signature prediction model,” Proc. SPIE 4209, 249–267 (1999).
[Crossref]

S. P. Sullivan and W. R. Reynolds, “Validation of the physically reasonable infrared signature model (PRISM),” Proc. SPIE 0890, 104–110 (1988).
[Crossref]

S. E. Lane, C. S. Nichols, A. M. Spencer, and J. M. Cathcart, “OREOS: a new EO-IR modeling and simulation tool for US Coast Guard search and rescue applications,” Proc. SPIE 8713, 87130R (2013).
[Crossref]

S. R. Lach, S. D. Brown, and J. P. Kerekes, “Semi-automated DIRSIG scene modeling from 3D LiDAR and passive imaging sources,” Proc. SPIE 6214, 62140I (2006).
[Crossref]

X. Xiong, F. Zhou, X. Bai, and X. Yu, “IR characteristic simulation of city scenes based on radiosity model,” Proc. SPIE 8907, 890727 (2013).
[Crossref]

B. Bartos and K. Stein, “FTOM-2D: a two-dimensional approach to model the detailed thermal behavior of nonplanar surfaces,” Proc. SPIE 9653, 96530G (2015).
[Crossref]

Z. Wang, Z. Jiang, S. Liu, and Q. Peng, “New model for realistic IR image rendering of city buildings,” Proc. SPIE 5405, 177–188 (2004).
[Crossref]

A. Malaplate, P. Grossmann, and F. Schwenger, “CUBI: a test body for thermal object model validation,” Proc. SPIE 6543, 654305 (2007).
[Crossref]

Other (22)

MODTRAN, “MODTRAN atmospheric radiative transfer model,” http://www.modtran.org .

T. Foken and C. J. Nappo, Micrometeorology (Springer, 2008), Chap. 4.

F. D. Lapierre and M. Acheroy, “Modelisation of convective heat transfer using novel adaptive parametric models and novel empirical models obtained from measured surface temperatures,” in 5th International IR Target, Background Modelling & Simulation (ITBMS) Workshop (2009).

Solar Air Conditioning Tech Group: solar-ac@yahoogroups.com, “Absorptivity & Emissivity table 1 plus others,” http://www.solarmirror.com/fom/fom-serve/cache/43.html .

J. A. Clarke, P. P. Yaneske, and A. A. Pinney, “The Harmonisation of Thermal Properties of Building Materials,” http://www.esru.strath.ac.uk .

N. Aspert, D. Santa Cruz, and T. Ebrahimi, “MESH: measuring errors between surfaces using the Hausdorff distance,” in Pcoceedings of IEEE International Conference on Multimedia and Expo (IEEE, 2002), 5705–5708.

Q.-Y. Zhou and U. Neumann, “Fast and extensible building modeling from airborne LiDAR data,” in Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM, 2008), 1–8.

C. Poullis and S. You, “Automatic reconstruction of cities from remote sensor data,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), 2775–2782.

M. Kazhdan, M. Bolitho, and H. Hoppe, “Poisson surface reconstruction,” in Proceedings of the Fourth Eurographics Symposium on Geometry Processing (Eurographics Association, 2006), 61–70.

A. Hornung and L. Kobbelt, “Robust reconstruction of watertight 3D models from non-uniformly sampled point clouds without normal information,” in Proceedings of Symposium on Geometry Processing (Eurographics Association, 2006), 41–50.

V. Lempitsky and Y. Boykov, “Global optimization for shape fitting,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), 1–8.

A.-L. Chauve, P. Labatut, and J.-P Pons, “Robust piecewise-planar 3D reconstruction and completion from large-scale unstructured point data,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), 1261–1268.

Q.-Y. Zhou and U. Neumann, “2.5D building modeling by discovering global regularities,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), 326–333.

F. Lafarge, “Some new research directions to explore in urban reconstruction,” in Proceedings of Joint Urban Remote Sensing Event (IEEE, 2015), 1–4.

J. M. Coughlan and A.L. Yuille, “The Manhattan world assumption: Regularities in scene statistics which enable Bayesian inference,” in Conference on Neural Information Processing Systems (MIT Press, 2000), 845–851.

K. Johnson, A. Curran, D. Less, D. Levanen, E. Marttila, T. Gonda, and J. Jones, “MuSES: A new heat and signature management design tool for virtual prototyping,” in Proceedings of the 9th Annual Ground Target Modelling & Validation Conference (Citeseer, 1998).

M. Jancosek and T. Pajdla, “Multi-view reconstruction preserving weakly-supported surfaces,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), 3121–3128.

M. Attene, S. Katz, M. Mortara, G. Patané, M. Spagnuolo, and A. Tal, “Mesh segmentation – A comparative study,” in Proceedings of IEEE International Conference on Shape Modeling and Applications (IEEE, 2006), 7.

F. Lafarge, R. Keriven, and M. Brédif, “Combining meshes and geometric primitives for accurate and semantic modeling,” in The British Machine Vision Conference (BMVA, 2009), 38.1–38.11.

D. Cohen-Steiner and J. Morvan, “Restricted Delaunay triangulations and normal cycle,” in Proceedings of the Nineteenth Annual Symposium on Computational Geometry (ACM, 2003), 312–321.

B. Heckel, A. E. Uva, B. Hamann, and K. I. Joy, “Surface reconstruction using adaptive clustering methods,” in Geometric Modelling (Springer, 2001), 199–218.
[Crossref]

S. E. Paul, “Subpixel temperature estimation from single-band thermal infrared imagery,” Ph.D. thesis, Rochester Institute of Technology (2012).

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Figures (16)

Fig. 1
Fig. 1

An overview of the proposed simulation pipeline: (a) A sequence of multi-view images acquired by a hand-held camera; (b) Mesh model generated by using MVS; (c) Superfacets clustered through region growing; (d) Superfacets labeled with different classes of interest; (e) Plane proxies extracted from superfacets; (f) Optimized geometric model produced via surface reconstruction; (g) Subdivided element meshes of model surface; (h) Model with assigned material attributes; (i) Simulated infrared scene.

Fig. 2
Fig. 2

Classification of superfacets into semantic groups: (a) Input mesh; (b) Clustered superfacets (based on curvature information); (c) Aggregated superfacets (according to the rules of our hierarchy algorithm); (d) Classified superfacets with labels of semantic groups. Color code used here: roof (blue), facade (yellow), and ground (brown).

Fig. 3
Fig. 3

Proxy regularization: (a) For each individual building component, we construct the local coordinate system. (b) 22 plane proxies are extracted from the superfacets and then divided into (c) 7 parallel proxy clusters and (d) 22 coplanar proxy sets. (e) A relationship graph is constructed, where each node represents a parallel cluster, and the edge between two nodes represents their geometric relationships, such as orthogonality (purple), V-symmetry (blue), and H-symmetry (yellow). These proxies are regularized according to their geometric constraints that are illustrated in the graph.

Fig. 4
Fig. 4

Orientation alignment of proxies: purple, blue and yellow circles represent the relationships of orthogonality, V-symmetry, and H-symmetry respectively. Only when two circles intersect, the new orientation (red arrow) can be uniquely calculated as the direction head to the intersection point. If only one circle exists, the old orientation (gray arrow) is projected orthogonally onto the circle.

Fig. 5
Fig. 5

Placement alignment of proxies.

Fig. 6
Fig. 6

2D illustration of surface reconstruction: (a) Complete arrangement partition as in [25]; (b) Constraint arrangement partition of our method; (c) Anchors that are marked as inside (red) or outside (black) and their containing cells; (d) Combination of inside labelling cells. Solid line indicates splitting plane, red dot indicates inside anchor and black dot indicates outside anchor.

Fig. 7
Fig. 7

Mesh regeneration: (a) Input mesh; (b) Simplified polygonal model; (c) Subdivided mesh elements with assigned material.

Fig. 8
Fig. 8

Schematic view of the heat exchange processes.

Fig. 9
Fig. 9

Model generation accuracy and structure awareness. From top to bottom are the results of three datasets: (a) Classical museum; (b) Laboratory building; (c) Man-made brick buildings. The first column is input mesh reconstructed by MVS. The second column and the third column show the geometric model and its Hausdorff distance to the input mesh, which is generated by our method and the LOD algorithm in [25] respectively. The Hausdorff distance displays with color scale from blue to red, which represents the value from low to high.

Fig. 10
Fig. 10

Left: Meshes attached with material characteristics. Right: Man-made buildings on the test field. Solid dots and dashed dots with numbers indicate thermistor locations on the front facets and back facets respectively. Thermistor 1 (south wall facet), 2 (east wall facet), 3 (north wall facet), 4 (west wall facet), 5 (south roof facet), 6 (east roof facet), 7 (north roof facet), and 8 (west roof facet).

Fig. 11
Fig. 11

Measured temperatures (black curve) and temperatures calculated by our approach for all test facets (1–8 in Fig. 10), beginning from 11:00 to 16:00 (UTC +8) on April 26th, 2013.

Fig. 12
Fig. 12

Deviation of temperature between the calculated and measured results for different direction offsets of test facets (1–8 in Fig. 10), beginning from 11:00 to 16:00 (UTC +8) on April 26th, 2013. H indicates the offset of horizontal azimuth angle (“+” represents clockwise direction, “−” represents counterclockwise direction); V indicates the offset of pitch angle (“+” represents direction off the ground, “−” represents direction facing the ground). The two dash lines are set at ±2K, which indicate fixed relevance criterions.

Fig. 13
Fig. 13

The comparison of simulation result with different geometric models: (a) Infrared image taken by a FLIR A615 camera; (b) Simulated scene with our geometric model; (c) Simulated scene with mesh reconstructed by multi-view stereo.

Fig. 14
Fig. 14

Various simulated and real IR images of scenes at different times on April 26th, 2013. The first row (subfigure (a) – (c)) and the third row (subfigure (g) – (i)) are simulated scene images from 11:00 to 16:00 in long wave IR (7.5μm – 14μm) band. The second row (subfigure (d) – (f)) and the fourth row (subfigure (j) – (l)) are recorded IR images from 11:00 to 16:00 in the same waveband.

Fig. 15
Fig. 15

Model generation and infrared simulation on small scale scenes. From top to bottom, the results of the three datasets are shown: museum, laboratory building, and man-made brick buildings. The first column are input mesh models. The second column are optimized geometric models generated by our method. The semantic structures of the input mesh are well preserved. The third column are subdivided mesh elements attached with material characteristics. The fourth column displays simulated scenes in long wave IR (7.5μm – 14μm) band. IR signatures of the walls and roofs as well as IR shadows are realistically calculated and rendered.

Fig. 16
Fig. 16

Model generation and infrared simulation on a large scale scene. In subfigure (b) – (e), the red squares mark the area corresponding to the red border image in subfigure (a) and give detailed views at the same perspective.

Tables (3)

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Table 1 Parameters Used in Geometric Model Reconstruction

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Table 2 Output Complexity and Accuracy

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Table 3 Parameters Used in Infrared Simulation

Equations (19)

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α e ( f i ) = ( h i h min h max h min ) 1 / 2
α p ( f i ) = 1 3 | λ min | 1 / 2 | λ min | 1 / 2 + | λ mid | 1 / 2 + | λ max | 1 / 2
α h ( f i ) = | n i n z |
U ( l ) = i S U d ( l i ) + γ { i , j } E U s ( l i , l j )
U d ( l i ) = A i { 1 α p α h α ¯ e if l i = ground 1 α ¯ p α h if l i = vegetation 1 α p α ¯ h if l i = facade 1 α p α h α e if l i = roof
U s ( l i , l j ) = C i j | n i n j | δ ( l i , l j )
θ ( n ) = arccos ( n n z )
φ ( n ) = arctan ( n n y n n x )
E ( S ) = c i C out V c i p ( c i ) + c i C in V c i ( 1 p ( c i ) ) + β f j S A f j
p ( c i ) = ( 2 r in 1 ) | 2 r in 1 | α + 1 2
d T s d t ρ d l C p = R + C + L
R = α E sun + ε E sky ε σ T s 4
C = ( h 1 + h 2 v air ) ( T air T s )
L = r ( h 1 + h 2 v air ) ( e ( T air , h r ) e ( T s , 100 % ) )
d T s d t ρ d l C p = α E sun + ε E sky ε σ T s 4 + ( h 1 + h 2 v air ) ( T air T s ) + r ( h 1 + h 2 v air ) ( e ( T air , h r ) e ( T s , 100 % ) ) + k d l ( T 2 T 1 )
d T n d t ρ d l C p = k d l ( T n 1 2 T n + T n + 1 )
d T N d t ρ d l C p = k d l ( T N 1 2 T N + T C )
E i = ε i λ 1 λ 2 c i λ 5 1 exp ( c 2 λ T i ) 1 d λ , i = 1 , 2 , , N
G i = E i E min E max E min × 255

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