Adaptive segmentation for multi-view stereo

摘要

This study presents an adaptive segmentation method for pre-processing input data to the patch-based multi-view stereo algorithm. A specially developed greyscale transformation is applied to the input image data, thus redefining the intensity histogram. The Nelder-Mead simplex method is used to adaptively locate an optimised segmentation threshold point in the modified histogram. The transformed input image is then segmented using the acquired threshold value, into foreground and background data. The segmentation information acquired is applied to the initial feature extraction and the cyclic patch-expansion procedure to constrain the reconstruction to a three-dimensional visibility space that excludes background artefacts. The method is targeted at segmenting out potentially disruptive data and is able to realise a reduction in cumulative error of the reconstruction process and thus improve the final reconstruction. With this method, the authors obtain results that are relatively similar to the original patch-based method, but with reduced time and space complexity.