Abstract
For future 3D TV broadcasting systems and navigation applications, it is necessary to have accurate stereo matching which could precisely estimate depth map from two distanced cameras. In this paper, we first suggest a trinary cross color (TCC) census transform, which can help to achieve accurate disparity raw matching cost with low computational cost. The two-pass cost aggregation (TPCA) is formed to compute the aggregation cost, then the disparity map can be obtained by a range winner-take-all (RWTA) process and a white hole filling procedure. To further enhance the accuracy performance, a range left-right checking (RLRC) method is proposed to classify the results as correct, mismatched, or occluded pixels. Then, the image-based refinements for the mismatched and occluded pixels are proposed to refine the classified errors. Finally, the image-based cross voting and a median filter are employed to complete the fine depth estimation. Experimental results show that the proposed semi-global stereo matching system achieves considerably accurate disparity maps with reasonable computation cost.
Highlights
The measure of the distance of the scene for robotic systems [1, 2], self-directed vehicles [3], or 3D video broadcasting systems [4, 5] is an important research topic in computer vision
To achieve high-precision stereo matching, we propose a semi-global stereo matching system with the trinary cross color (TCC) census transform to reduce sensitivity in smooth region, the two-pass cost aggregation (TPCA) to obtain stable cost, the range winnertake-all (RWTA) to select the robust depth, and the range left-right check (RLRC) to keep the reliable depth
We propose a semi-global stereo matching system based on several techniques, including the TCC census, TPCA, range winner-take-all (RWTA), and RLRC methods as well as image-based refinements to achieve highprecision depth estimation
Summary
The measure of the distance of the scene for robotic systems [1, 2], self-directed vehicles [3], or 3D video broadcasting systems [4, 5] is an important research topic in computer vision. To achieve high-precision stereo matching, we propose a semi-global stereo matching system with the trinary cross color (TCC) census transform to reduce sensitivity in smooth region, the two-pass cost aggregation (TPCA) to obtain stable cost, the range winnertake-all (RWTA) to select the robust depth, and the range left-right check (RLRC) to keep the reliable depth. We propose a semi-global stereo matching system based on several techniques, including the TCC census, TPCA, RWTA, and RLRC methods as well as image-based refinements to achieve highprecision depth estimation. The TCC census transform, which uses trinary census with the cross-square pattern is suggested to compute the raw matching cost hereafter. The smooth terms in the row and column directions could reduce the overall matching error rates, and the modified two-pass cross-based adaptive support weight cost aggregation produces a robust rough disparity maps.
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