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

  • Authors:

  • Deepak Dwarakanath;Alexander Eichhorn;Pål Halvorsen;Carsten Griwodz

  • Affiliations:

  • Simula Research Laboratory, University of Oslo, Oslo, Norway;Simula Research Laboratory, Oslo, Norway;Simula Research Laboratory, University of Oslo, Oslo, Norway;Simula Research Laboratory, University of Oslo, Oslo, Norway

  • Venue:
  • Proceedings of the 27th Conference on Image and Vision Computing New Zealand
  • Year:
  • 2012

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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.