Abstract
As an essential step in 3D reconstruction, stereo matching still faces unignorable problems due to the high resolution and complex structures of remote sensing images. Especially in occluded areas of tall buildings and textureless areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we develop a novel edge-sense bidirectional pyramid stereo matching network to solve the aforementioned problems. The cost volume is constructed from negative to positive disparities since the disparity range in remote sensing images varies greatly and traditional deep learning networks only work well for positive disparities. Then, the occlusion-aware maps based on the forward-backward consistency assumption are applied to reduce the influence of the occluded area. Moreover, we design an edge-sense smoothness loss to improve the performance of textureless areas while maintaining the main structure. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two methods, DenseMapNet and PSMNet, in terms of averaged endpoint error (EPE) and the fraction of erroneous pixels (D1), and the improvements in occluded and textureless areas are significant.
Highlights
With the rapid growth of remote sensing image resolution and data volume, the way we observe the Earth is no longer limited to two-dimensional images
The main contributions of our work can be summarized in three points: (1) we reconstruct the cost volume and reset the range of disparity regression to estimate both positive and negative disparity maps in remote sensing images; (2) we present a bidirectional unsupervised loss to solve the Remote Sens. 2020, 12, 4025 disparity estimation in occluded areas; (3) we propose an edge-sense smoothness loss to improve the performance in the textureless regions without blurring the main structure
It is clear that the proposed network gives the best results, and on JAX it raises by about 6% and 15% in terms of endpoint error (EPE) and D1 compared to the PSMNet
Summary
With the rapid growth of remote sensing image resolution and data volume, the way we observe the Earth is no longer limited to two-dimensional images. Multiview remote sensing images acquired from different angles provide the foundation to reconstruct the three-dimensional structures of the world. 3D reconstruction from multiview remote sensing images has been applied to various fields including urban modeling, environment research, and geographic information systems. As the essential step of 3D reconstruction, stereo matching finds dense correspondences from a pair of rectified stereo images, resulting in pixelwise disparities. Stereo matching algorithms can be divided into four steps: matching cost computation, cost aggregation, cost optimization, and disparity refinement [1]. The Semi-Global Matching (SGM) algorithm [2], which optimizes the global energy function with aggregation in many directions, is the most widely used method for cost aggregation. Several improvements of the SGM method have been proposed by designing more robust matching cost functions [3,4]
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