3D Lunar Terrain Reconstruction from Apollo Images
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
A portable stereo vision system for whole body surface imaging
Image and Vision Computing
A Bayesian formulation for sub-pixel refinement in stereo orbital imagery
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Lunar terrain and albedo reconstruction of the apollo 15 zone
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Improving sub-pixel accuracy for long range stereo
Computer Vision and Image Understanding
How Accurate Can Block Matches Be in Stereo Vision?
SIAM Journal on Imaging Sciences
Improving sub-pixel correspondence through upsampling
Computer Vision and Image Understanding
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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.