Efficient Stereo Matching Based on Pervasive Guided Image Filtering

Abstract

This paper presents an effective cost aggregation strategy for dense stereo matching. Based on the guided image filtering (GIF), we propose a new aggregation scheme called Pervasive Guided Image Filtering (PGIF) to introduce weightings to the energy function of the filter which allows the whole image pair to be taken into account. The filter parameters of PGIF are calculated as two-dimensional convolution using the bright and spatial differences between the corresponding pixels, which can be incrementally calculated for efficient aggregation. The complexity of the proposed algorithm is O(N), which is linear to the number of image pixels. Furthermore, the algorithm can be further simplified into O(N/4) without significantly sacrificing accuracy if subsampling is applied in the stage of parameter calculation. We also found that a step function to attenuate noise is required in calculating the weights. Experimental evaluation on version 3 of the Middlebury stereo evaluation datasets shows that the proposed method achieves superior disparity accuracy over state-of-the-art aggregation methods with comparable processing speed.

Hierarchical Guided-Image-Filtering for Efficient Stereo Matching

“…This section presents the basic procedure of the pervasive guided-image-filtering (PGIF) [25] scheme. It serves as the definitions of some of the variables and algorithms for the proposed scheme to be shown in the next section.…”

“…In the GIF-based stereo matching algorithms, such as WGIF [21], GDGIF [22], and PGIF [25] are discussed in the last section. The aggregated cost, C d,0 (p) is a linear function of the guidance image, I G at a local disparity patch such that:…”

“…However, information outside the support windows is still missing. In a recent paper [25], we introduced weights that take both distance and intensity differences into account to extend the scheme of GIF. We called our approach the pervasive guided-image-filtering, denoted as PGIF [25], which exploits the whole image for aggregation.…”

“…Inspired by the multiscale scheme of Zhang [29], we extend the pervasive guided-image-filtering, PGIF [25], to exploit the cross-scale features in the cost volume. In our approach, the consistency is imposed on the GIF parameters, rather than the cost volume of Zhang [29], in the neighboring-scale direction.…”

“…(1) We created an innovative aggregation approach that efficiently combines the model parameters of PGIF [25] to allow the features of the image pairs in different resolutions to be considered; (2) The scheme is unique in its parameter-based aggregation, rather than the cost-volume-based approaches in the current literature, allowing efficient calculation with superior performance; (3) The proposed scheme outperforms most of the state-of-art algorithms in terms of disparity accuracy even without the refinement procedure.…”