A novel object-oriented stereo matching on multi-scale superpixels for low-resolution depth mapping

This paper presents a novel object-oriented stereo matching on multi-scale superpixels to generate a low-resolution depth map. It overcomes the classic downsampling methods´ disadvantages, such as boundary blurring, outlier enlargement and foreground objects merging to background, etc. The approach we exploited is to segment the image in three scales´ superpixels from dense to sparse ones according to downsampling scale first, then compute disparity directly on superpixel´s stereo matching. The post-processing of region constraint and local refinement uses hierarchical multi-scale superpixels as well. The proposed approach is validated on Middle-bury test-bed, and the experimental results outperform the current state-of-the-art stereo matching methods in low resolutions.