Progressively weighted affine adaptive correlation matching for quasi-dense 3D reconstruction
- 作者：Dongjoe Shin ; ds2@mssl.ucl.ac.uk ; [Author Vitae] ; Jan-Peter Muller jpm@mssl.ucl.ac.uk ; [Author Vitae]
- 关键词：Adaptive least squares correlation ; Relaxation labelling ; Scale invariant feature matching ; Affine invariant matching ; Dense stereo reconstruction
- 刊名：Pattern Recognition
- 出版年：2012
- 期刊代码：100_00313203
- 类别：cp
- 出版时间：October, 2012
- 卷：45
- 期：10
- 页码：3795-3809
- 文件大小：2560 K
摘要
Correlation matching has been widely accepted as a rudimentary similarity measure to obtain dense 3D reconstruction from a stereo pair. In particular, given a large overlapping area between images with minimal scale differences, the correlation results followed by a geometrically constrained global optimisation delivers adequately dense and accurate reconstruction results. In order to achieve greater reliability, however, correlation matching should correctly account for the geometrical distortion introduced by the different viewing angles of the stereo or multi-view sensors. Conventional adaptive least squares correlation (ALSC) matching addresses this by modifying the shape of a matching window iteratively, assuming that the distortion can be approximated by an affine transform. Nevertheless, since an image captured from different viewing angle is often not practically identical due to scene occlusions, the matching confidence normally deteriorates. Subsequently, it affects the density of the reconstruction results from ALSC-based stereo region growing algorithms. To address this, we propose an advanced ALSC matching method that can progressively update matching weight for each pixel in an aggregating window using a relaxation labelling technique. The experimental results show that the proposed method can improve matching performance, which consequently enhances the quality of stereo reconstruction. Also, the results demonstrate its ability to refine a scale invariant conjugate point pair to an affine and scale invariant point pair.