The unknown spatial quality of dense point clouds derived from stereo images

IEEE Geoscience and Remote Sensing Letters

Volumn 12, Issue 5, 2015, Pages 1013-1017

  Jalobeanu, André ^a   Gonçalves, Gil ^b,c  

^a BayesMap Solutions   (United States)

^b UNIVERSITY OF COIMBRA   (Portugal)

^c INESC COIMBRA   (Portugal)

Author keywords

Error analysis; image matching; image processing; point clouds; probability; spatial quality; uncertainty

Indexed keywords

ERROR ANALYSIS; ERRORS; IMAGE MATCHING; IMAGE PROCESSING; PROBABILITY; UNCERTAINTY ANALYSIS;

FUNDAMENTAL LIMITATIONS; INDEPENDENT MEASUREMENT; PHOTOGRAMMETRIC SOFTWARE; POINT CLOUD; SPATIAL QUALITY; STATE-OF-THE-ART ALGORITHMS; STATISTICAL SIGNIFICANCE; UNCERTAINTY;

STEREO IMAGE PROCESSING;

EID: 85027923037     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2014.2373133     Document Type: Article

References (26)

1. F. Leberl et al., "Point clouds: LiDAR versus 3-D vision," Photogram. Eng. Remote Sens., vol. 76, no. 10, pp. 1123-1134, Oct. 2010.
2. S. Gehrke, K. Morin, M. Downey, N. Boehrer, and T. Fuchs, "Semi-global matching: An alternative to LiDAR for DSM generation?" Proc. ISPRS Archives, Calgary, AB, Canada, 2010, vol. 38, no. 1, pp. 1-6.
3. E. T. Jaynes, Probability Theory: The Logic of Science, Cambridge, U.K.: Cambridge Univ. Press, 2003.
4. W. Foerstner, "On the geometric precision of digital correlation," in Proc. Symp. ISPRS Commission III, Int. Archives Photogramm., vol. 24 III, Helsinki, Finland, 1982, pp. 176-189.
5. F. Ackermann, "Digital image correlation: Performance and potential application in photogrammetry," Photogramm. Rec., vol. 11, no. 64, Oct. 1984.
6. A. Gruen, "Adaptive least squares correlation: A powerful image matching technique," Remote Sens. Cartography South Afr. J. Photogram., vol. 14, no. 3, pp. 175-187, 1985.
7. J. Sun, N.-N. Zheng, and H.-Y. Shum, "Stereo matching using belief propagation," IEEE Trans. Pattern Analysis Mach. Intell., vol. 25, no. 7, pp. 787-800, pp. 787-800, Jul. 2003.
8. F. J. Aguilar et al., "Modelling vertical error in LiDAR-derived digital elevation models," ISPRS J. Photogram. Remote Sens., vol. 65, no. 1, pp. 103-110, Jan. 2010.
9. A. Jalobeanu, A. Kim, S. Runyon, R. Olsen, and F. Kruse, "Uncertainty assessment and probabilistic change detection using terrestrial and airborne LiDAR," in Proc. SPIE Laser Radar Technol. Appl. 19, vol. 9080, Jun. 2014, pp. 1-19.
10. S. Wechsler and C. Kroll, "Quantifying DEM uncertainty and its effect on topographic parameters," Photogram. Eng. Remote Sens., vol. 72, no. 9, pp. 1081-1090, Sep. 2006.
11. N. Haala, "The landscape of dense image matching algorithms," in Proc. Photogramm. Week '13, D. Fritsch, Ed., Stuttgart, Germany, 2013, pp. 271-284.
12. O. Faugeras, Q.-T. Luong, and T. Papadopoulo, The Geometry of Multiple Images-The Laws That Govern the Formation of Multiple Images of a Scene and Some of Their Applications. Cambridge, MA, USA: MIT Press, 2001.
13. C. Davis, H. Jiang, and X. Wang, "Modeling and estimation of the spatial variation of elevation error in high resolution DEMs from stereoimage processing," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 11, Nov. 2001.
14. M. Brown, D. Burschka, and G. Hager, "Advances in computational stereo," IEEE Trans. Pattern Analysis Mach. Intell., vol. 28, no. 8, Aug. 2003.
15. A. Gruen, "Development and status of image matching in photogramme-try," Photogramm. Rec., vol. 27, no. 137, pp. 36-57, Mar. 2012.
16. A. Jalobeanu, "Predicting spatial uncertainties in stereo photogrammetry: Achievements and intrinsic limitations," presented at the 7th Int. Symp. Spatial Data Quality, Coimbra, Portugal, Oct. 2011.
17. X. Hu and P. Mordohai, "A quantitative evaluation of confidence measures for stereo vision," IEEE Trans. Pattern Analysis Mach. Intell., vol. 34, no. 11, pp. 2121-2133, Nov. 2012.
18. J. M. Bernardo and A. F. M. Smith, Bayesian Theory, New York, NY, USA: Wiley, 1994.
19. A. Jain, Fundamentals of Digital Image Processing, Englewood Cliffs, NJ, USA : Prentice Hall, 1989.
20. J. Inglada, V. Muron, D. Pichard, and T. Feuvrier, "Analysis of artifacts in subpixel remote sensing image registration," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 1, pp. 254-264, Jan. 2007.
21. H. Hirschmuller, "Accurate and efficient stereo processing by semiglobal matching and mutual information," in Proc. CVPR, 2005, vol. 2, pp. 807-814.
22. M. Rumpler, A. Irschara, and H. Bischof, "Multi-view stereo: Redundancy benefits for 3-D reconstruction," presented at the 35th Workshop AAPR/OAGM, 2011.
23. E. Tola, C. Strecha, and P. Fua, "Efficient large-scale multi-view stereo for ultra high-resolution image sets," Mach. Vis. Appl., vol. 23, no. 5, pp. 903-920, Sep. 2012.
24. J. Fox, Nonparametric Simple Regression: Smoothing Scatterplots, Thousand Oaks, CA, USA: Sage, 2000.
25. M. Kendall and A. Stuart, The Advanced Theory of Statistics, Volume 2: Inference and Relationship, 3rd ed. London, U.K.: Griffin, 1973.
26. E. Tola, V. Lepetit, and P. Fua, "DAISY: An efficient dense descriptor applied to wide-baseline stereo," IEEE Trans. Pattern Analysis Mach. Intell., vol. 32, no. 5, May 2010.