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A Quantitative Evaluation of Confidence Measures for Stereo Vision
Nov. 2012 (vol. 34 no. 11)
pp. 2121-2133
Xiaoyan Hu, Dept. of Comput. Sci., Stevens Inst. of Technol., Hoboken, NJ, USA
P. Mordohai, Dept. of Comput. Sci., Stevens Inst. of Technol., Hoboken, NJ, USA
We present an extensive evaluation of 17 confidence measures for stereo matching that compares the most widely used measures as well as several novel techniques proposed here. We begin by categorizing these methods according to which aspects of stereo cost estimation they take into account and then assess their strengths and weaknesses. The evaluation is conducted using a winner-take-all framework on binocular and multibaseline datasets with ground truth. It measures the capability of each confidence method to rank depth estimates according to their likelihood for being correct, to detect occluded pixels, and to generate low-error depth maps by selecting among multiple hypotheses for each pixel. Our work was motivated by the observation that such an evaluation is missing from the rapidly maturing stereo literature and that our findings would be helpful to researchers in binocular and multiview stereo.
Index Terms:
Cost function,Estimation,Reliability,Error analysis,Stereo image processing,Benchmark testing,Pattern matching,distinctiveness,Stereo vision,3D reconstruction,confidence,correspondence
Citation:
Xiaoyan Hu, P. Mordohai, "A Quantitative Evaluation of Confidence Measures for Stereo Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2121-2133, Nov. 2012, doi:10.1109/TPAMI.2012.46
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