NDLI: Evaluating performance of feature extraction methods for practical 3D imaging systems

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Evaluating performance of feature extraction methods for practical 3D imaging systems

Content Provider	ACM Digital Library
Author	Dwarakanath, Deepak Halvorsen, Pål Eichhorn, Alexander Griwodz, Carsten
Abstract	Smart cameras are extensively used for multi-view capture and 3D rendering applications. To achieve high quality, such applications are required to estimate accurate position and orientation of the cameras (called as camera calibration-pose estimation). Traditional techniques that use checkerboard or special markers, are impractical in larger spaces. Hence, feature-based calibration (auto-calibration), is necessary. Such calibration methods are carried out based on features extracted and matched between stereo pairs or multiple cameras. Well known feature extraction methods such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-Up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) have been used for auto-calibration. The accuracy of auto-calibration is sensitive to the accuracy of features extracted and matched between a stereo pair or multiple cameras. In practical imaging systems, we encounter several issues such as blur, lens distortion and thermal noise that affect the accuracy of feature detectors. In our study, we investigate the behaviour of SIFT, SURF and ORB through simulations of practical issues and evaluate their performance targeting 3D reconstruction (based on epipolar geometry of a stereo pair). Our experiments are carried out on two real-world stereo image datasets of various resolutions. Our experimental results show significant performance differences between feature extractors' performance in terms of accuracy, execution time and robustness to blur, lens distortion and thermal noise of various levels. Eventually, our study identifies suitable operating ranges that helps other researchers and developers of practical imaging solutions.
Starting Page	250
Ending Page	255
Page Count	6
File Format	PDF
ISBN	9781450314732
DOI	10.1145/2425836.2425887
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-11-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Surf Sift 3d applications Auto calibration Stereo matching performance Feature detection & extraction Orb
Content Type	Text
Resource Type	Article

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