A simplified ICA-based local similarity stereo matching

Abstract

AbstractSince the existing stereo matching methods may fail in the regions of non-textures, boundaries and tiny details, a simplified independent component correlation algorithm (ICA)-based local similarity stereo matching algorithm is proposed. In order to improve the DispNetC, the proposed algorithm first offers the simplified independent component correlation algorithm (SICA) cost aggregation. Then, the algorithm introduces the matching cost volume pyramid, which simplifies the pre-processing process for the ICA. Also, the SICA loss function is defined. Next, the region-wise loss function combined with the pixel-wise loss function is defined as a local similarity loss function to improve the spatial structure of the disparity map. Finally, the SICA loss function is combined with the local similarity loss function, which is defined to estimate the disparity map and to compensate the edge information of the disparity map. Experimental results on KITTI dataset show that the average absolute error of the proposed algorithm is about 37% lower than that of the DispNetC, and its runtime consuming is about 0.6 s lower than that of GC-Net.