A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
An Experimental Comparison of Stereo Algorithms
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Prediction Error as a Quality Metric for Motion and Stereo
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Range image quality assessment by Structural Similarity
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
An Objective Evaluation for Disparity Map Based on the Disparity Gradient and Disparity Acceleration
ITCS '09 Proceedings of the 2009 International Conference on Information Technology and Computer Science - Volume 01
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Objective Quality Assessment of Noised Stereoscopic Images
ICMTMA '11 Proceedings of the 2011 Third International Conference on Measuring Technology and Mechatronics Automation - Volume 02
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Real-time dense stereo for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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The quantitative evaluation of disparity maps is based on error measures. Among the existing measures, the percentage of Bad Matched Pixels (BMP) is widely adopted. Nevertheless, the BMP does not consider the magnitude of the errors and the inherent error of stereo systems, in regard to the inverse relation between depth and disparity. Consequently, different disparity maps, with quite similar percentages of BMP, may produce 3D reconstructions of largely different qualities. In this paper, a ground-truth based measure of errors in estimated disparity maps is presented. It offers advantages over the BMP, since it takes into account the magnitude of the errors and the inverse relation between depth and disparity. Experimental validations of the proposed measure are conducted by using two state-of-the-art quantitative evaluation methodologies. Obtained results show that the proposed measure is more suited than BMP to evaluate the depth accuracy of the estimated disparity map.