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A Novel Confidence Measure for Disparity Maps by Pixel-Wise Cost Function Analysis

: Op het Veld, Ron; Jaschke, Tobias; Bätz, Michel; Palmieri, Luca; Keinert, Joachim

Postprint urn:nbn:de:0011-n-5593715 (150 KByte PDF)
MD5 Fingerprint: efcd226c14166c738f199a5456f6d1dd
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Erstellt am: 3.10.2019

Institute of Electrical and Electronics Engineers -IEEE-:
25th IEEE International Conference on Image Processing, ICIP 2018 : October 7-10, 2018, Athens, Greece
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-4799-7061-2
ISBN: 978-1-4799-7062-9
International Conference on Image Processing (ICIP) <25, 2018, Athens>
European Commission EC
H2020; 676401; ETN-FPI
European Training Network on Full Parallax Imaging
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IIS ()
stereo vision; 3D reconstruction; disparity estimation; confidence measure; CNN

Disparity estimation algorithms mostly lack information about the reliability of the disparities. Therefore, errors in initial disparity maps are propagated in consecutive processing steps. This is in particularly problematic for difficult scene elements, e.g., periodic structures. Consequently, we introduce a simple, yet novel confidence measure that filters out wrongly computed disparities, resulting in improved final disparity maps. To demonstrate the benefit of this approach, we compare our method with existing state-of-the-art confidence measures and show that we improve the ability to detect false disparities by 54.2%.