DocumentCode :
272426
Title :
The Unknown Spatial Quality of Dense Point Clouds Derived From Stereo Images
Author :
Jalobeanu, André ; Goncalves, Gil
Author_Institution :
Appl. Res. Labs., Univ. of Texas at Austin, Austin, TX, USA
Volume :
12
Issue :
5
fYear :
2015
fDate :
May-15
Firstpage :
1013
Lastpage :
1017
Abstract :
Is it possible to use stereo images to generate point clouds and to compute rigorous uncertainty maps? Currently, neither modern commercial photogrammetric software nor state-of-the-art algorithms are able to provide a spatial distribution of uncertainty. In this letter, we explain why this is the case, despite a high demand from the user community. Many applications would indeed benefit from the availability of error bars on each point, as uncertainties on derived models and quantities could be accurately predicted. For instance, change detection could be performed rigorously since the statistical significance of observed changes could be computed. In this letter, we focus on dense stereo methods. We first explain that it is not possible to derive reliable predictive uncertainties mainly due to matching and modeling errors. Our research shows that both intrinsic and practical limitations of the algorithms lead to unpredictable artifacts. Then, we focus on the use of empirical errors, showing that, despite the redundancy brought by multiview stereo, there is a fundamental limitation due to the unknown density of independent measurements. We think that these problems will represent a big challenge for the future, as these limitations cannot be addressed by algorithmic design, computational power, or imaging sensor technology.
Keywords :
error analysis; geophysical image processing; image matching; remote sensing; stereo image processing; dense stereo methods; empirical errors; image matching; image processing; point clouds; stereo images; Computational modeling; Correlation; Data models; Noise; Radiometry; Three-dimensional displays; Uncertainty; Error analysis; image matching; image processing; point clouds; probability; spatial quality; uncertainty;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2014.2373133
Filename :
6985602
Link To Document :
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