Sparse Disparity Estimation Using Global Phase Only Correlation for Stereo Matching Acceleration

Abstract

In this study, we propose an efficient stereo matching method which estimates sparse disparities using global phase only correlation (POC). Conventionally, cost functions are to be calculated for all disparity candidates and the associated computational cost has been impediment in achieving a realtime performance. Therefore, we consider to use full image 2D phase only correlation (FIPOC) for detecting the valid disparities candidates. This would require comparatively fewer calculations for the same number of disparities. Since, the FIPOC output indicates the disparity distribution of two stereo images, we can sort the disparity candidates and choose them for sparse calculation. In our proposed method, the searchable disparity range is half of the input image size, which is much wider than that of the conventional methods. When we apply the FIPOC to naive sum of absolute difference (SAD) stereo matching method, the combined algorithm would require fewer calculations while maintaining the same accuracy. In our evaluation, the proposed method achieves 194 disparity stereo matching in 70 ms on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$398 \times 288$</tex> images without the need for SIMD instruction, multi-thread operation, or additional hardware while using a Intel Core i5-5257U.