Adaptive Threshold Clustering and Semi-Global Matching for Reliable Local Disparity Candidate Generation
Date Issued
2015
Date
2015
Author(s)
Huang, Shih-Chan
Abstract
We propose an algorithm that can provide disparity candidates of each pixel in the given input images in stereo matching problems. Most of the stereo matching algorithm can be separated in 2 parts: initial planes generation and optimization. We add some ideas trying to improve the former, and especially, we put emphasis on local plane generation. Our method estimates the planes from sparse feature matches, and we propose a strategy in collecting adjacent features with adaptive error threshold for plane fitting. The parameters of each plane is re-estimated whenever new feature points are incorporated according to the adaptive threshold setting. Furthermore, we apply the greedy aggregation: the plane with more features from the previous iteration is more probable for assigned features. We extract main planes fitting the scene structure without the burden of tuning the plane fitting error threshold. Finally we will apply semi-global matching to obtain the top five accurate candidates for each pixel.
Subjects
Daisy dense descriptors
adaptive threshold
semi-global matching
Local plane sweeps
Type
thesis
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