Improved sub-pixel stereo correspondences through symmetric refinement
- 1 January 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 557-563 Vol. 1
- https://doi.org/10.1109/iccv.2005.119
Abstract
Most dense stereo correspondence algorithms start by establishing discrete pixel matches and later refine these matches to sub-pixel precision. Traditional sub-pixel refinement methods attempt to determine the precise location of points, in the secondary image, that correspond to discrete positions in the reference image. We show that this strategy can lead to a systematic bias associated with the violation of the general symmetry of matching cost functions. This bias produces random or coherent noise in the final reconstruction, but can be avoided by refining both image coordinates simultaneously, in a symmetric way. We demonstrate that the symmetric sub-pixel refinement strategy results in more accurate correspondences by avoiding bias while preserving detail.Keywords
This publication has 13 references indexed in Scilit:
- Spacetime facesACM Transactions on Graphics, 2004
- Sampling the disparity space imagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Spacetime stereo: shape recovery for dynamic scenesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Optimal subpixel interpolation in particle image velocimetryExperiments in Fluids, 2003
- Computing rectifying homographies for stereo visionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Local, global, and multilevel stereo matchingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A taxonomy and evaluation of dense two-frame stereo correspondence algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Precise sub-pixel estimation on area-based matchingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Large Occlusion StereoInternational Journal of Computer Vision, 1999
- A pixel dissimilarity measure that is insensitive to image samplingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998