Kavli Affiliate: Feng Wang
| First 5 Authors: Liting Jiang, Yuming Xiang, Feng Wang, Hongjian You,
| Summary:
Stereo matching in remote sensing has recently garnered increased attention,
primarily focusing on supervised learning. However, datasets with ground truth
generated by expensive airbone Lidar exhibit limited quantity and diversity,
constraining the effectiveness of supervised networks. In contrast,
unsupervised learning methods can leverage the increasing availability of
very-high-resolution (VHR) remote sensing images, offering considerable
potential in the realm of stereo matching. Motivated by this intuition, we
propose a novel unsupervised stereo matching network for VHR remote sensing
images. A light-weight module to bridge confidence with predicted error is
introduced to refine the core model. Robust unsupervised losses are formulated
to enhance network convergence. The experimental results on US3D and WHU-Stereo
datasets demonstrate that the proposed network achieves superior accuracy
compared to other unsupervised networks and exhibits better generalization
capabilities than supervised models. Our code will be available at
https://github.com/Elenairene/CBEM.
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