Simplified High-Performance Cost Aggregation for Stereo Matching
Chengtao Zhu, Yau‐Zen Chang · Applied Sciences · 2023
Applying edge preservation filters for cost aggregation has been a leading technique in generating dense disparity maps. However, traditional approaches usually require intensive calculations, and their design parameters must be tuned for different scenarios to obtain the best performance. This paper shows that a simple texture-independent aggregation approach can achieve similar high performance. The proposed algorithm is equivalent to a sequence of matrix multiplications involving two weighting matrices and a primary matching cost. Notably, the weighting matrices are constant for image pairs with the same resolution. For higher matching accuracy, we integrate the algorithm with a multi-scale scheme to fully exploit the spatial distribution of textures in the image pairs. The resultant hybrid approach is efficient and accurate enough to surpass most existing approaches in stereo matching. The performance of the proposed approach is verified by extensive simulation results using the Middlebury (3rd Edition) benchmark stereo database.