Stereo Matching Based on Features of Image Patch
Stereo matching is a branch of 3D vision and has a wide range of applications in 3D reconstruction
and autonomous driving. Recently, stereo matching methods leverage all the information of the stereo image to calculate disparity map. However, these methods still have difficulties in texture-less areas and occlusion areas, and post-processing improves accuracy. Therefore, there is a high computational cost in feature extraction and post-processing. In this paper, we propose a stereo matching method based on features of image patches to predict the disparity of non-occlusion areas instead of full image features. And aggregation methods are performed to modify all kinds of mismatching pixels based on the correct disparity in the non-occlusion areas. Furthermore, we evaluated the proposed method on the Middlebury dataset. The result shows that the proposed method performs well in all areas.
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