Stereo Matching of VHR Remote Sensing Images via Bidirectional Pyramid Network

Abstract

Stereo matching, an essential step in 3D reconstruction, still faces unignorable problems due to the very high resolution and complex structures of remote sensing images. Especially in occluded areas of high buildings and untextured areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we propose a novel method based on the pyramid stereo matching network to solve the aforementioned problems. Inspired by the classical optical flow estimation framework, we adopt the forward-backward consistency assumption to improve the accuracy. Moreover, we improve the construction of cost volume since the traditional deep-learning networks only work well for positive disparities and the disparity ranges in remote sensing images vary a lot. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two baselines in terms of average endpoint error (EPE) and the fraction of erroneous pixels(D1), and the improvements in occluded areas are significant.