Cross-Scale Cost Aggregation for Stereo Matching

Abstract

Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.

Efficiency-enhanced cost-volume filtering featuring coarse-to-fine strategy

“…They showed that higher-quality disparity maps can be obtained by adding a regularization term between the cost values of different scales, and that the computational time of cross-scale aggregation is not significantly greater than that of the original CVF [28]. This method [49] is similar to ours in terms of multi-scale cost-volume utilization, but its purpose is to improve the quality of the disparity maps, not to reduce the computational complexity. Recently, Zhan et al [48] proposed some techniques for local stereo matching methods to improve the accuracy: mask filtering as a pre-processing, an improved matching cost function, and multi-step disparity refinement as a post-processing.…”

“…Further, Lu et al [21] showed that higher-quality stereo matching results can be achieved by applying the CLMF instead of the GF for cost aggregation. Zhang et al [49] proposed a cross-scale cost aggregation algorithm based on CVF [28] for stereo matching. They showed that higher-quality disparity maps can be obtained by adding a regularization term between the cost values of different scales, and that the computational time of cross-scale aggregation is not significantly greater than that of the original CVF [28].…”

“…We can classify them into two types: the coarse-to-fine strategies where the cost aggregation results from all resolution are merged in order to obtain more accurate results such as [33,42,49], and ones where the results of lower resolution are propagated to higher resolution in order to reduce the search range of labels such as [43,50]. We focus on the latter because our method is classified into latter group.…”

Stereo Matching Using Distance Trasnform and 1D Array Kernel

“…또한, 제안한 방법의 거리변환 지도 를 구하기 위해 Canny 경계 검출 방법으로 경계 영상 을 얻었다 [13] . 그림 9는 식 ( Conventional method [6] Proposed method…”

How Good Is My Test Data? Introducing Safety Analysis for Computer Vision

“…For evaluation of the stereo vision test dataset we used the following popular stereo vision algorithms: SAD + texture thresholding (TX) & connected component filtering (CCF) (Konolige 1998), SGBM + TX & CCF (Hirschmüller 2008), census-based BM + TX & CCF (Humenberger et al 2010;Kadiofsky et al 2012), cost-volume filtering (CVF) & weighted median post processing filtering (WM) , PatchMatch (PM) & WM , and cross-scale cost aggregation using census and segmenttrees (SCAA) & WM (Zhang et al 2014), (Mei et al 2013). The resulting disparities of each stereo vision algorithm are compared to the GT disparities of the test dataset.…”