Stereo Matching by Filtering-Based Disparity Propagation.

Xingzheng Wang,Yongbing Zhang,Haoqian Wang,Yushi Tian,Quan Zou

doi:10.1371/journal.pone.0162939

Xingzheng Wang, Yongbing Zhang + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0162939

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Abstract

Stereo matching is essential and fundamental in computer vision tasks. In this paper, a novel stereo matching algorithm based on disparity propagation using edge-aware filtering is proposed. By extracting disparity subsets for reliable points and customizing the cost volume, the initial disparity map is refined through filtering-based disparity propagation. Then, an edge-aware filter with low computational complexity is adopted to formulate the cost column, which makes the proposed method independent on the local window size. Experimental results demonstrate the effectiveness of the proposed scheme. Bad pixels in our output disparity map are considerably decreased. The proposed method greatly outperforms the adaptive support-weight approach and other conditional window-based local stereo matching algorithms.

Highlights

Stereo matching solves the correspondence problem between stereo image pairs, which for a long time has been one of the most fundamental and challenging computer vision tasks
Global approaches transform the problem to an energy-minimization model, which formulate a global optimization function composed of a data term and a smoothness term, and perform global disparity optimization [2,3,4,5,6,7,8,9] by dynamic programming (DP), graph cuts (GC) [10,11] or belief propagation (BP) [12,13,14,15]
We customize a new cost volume based on the initial disparity map and the disparity subsets of reliable points: (

Summary

Introduction

Stereo matching solves the correspondence problem between stereo image pairs, which for a long time has been one of the most fundamental and challenging computer vision tasks. The concept of semi-global stereo matching is proposed based on a recognition stage that the support pixels for cost aggregation should be selected from the whole image and not restricted in a local matching window. The high confidence disparity estimates are propagated from reliable points to unreliable ones by filtering a customized cost volume. A cost aggregation procedure which sums up costs in a window is usually implemented for local stereo matching In this way, cost aggregation is equivalent to applying filtering on the initial cost volume. The guided filter follows the local linear assumption, its computation complexity is unrelated to the local window size This property makes it more practical for cost volume filtering. It is reasonable that the information of reliable points in the cost volume has higher confidence and should do favor to other pixels

Building Disparity Subsets

Disparity Propagation Base on Customized Cost Volume

Integrating Disparity into Guided Filtering

Experimental Results

Conclusions