Recent years have witnessed tremendous success of single-image depth estimation. However, most of the existing approaches merely use scene descriptions of a whole image to retrieve its candidates, which may end up with undesirable depth supports for local regions. In this paper, we propose a segmentation method for single-image depth estimation based on data-driven framework. First, a per-pixel boundary spreading method is presented to improve the image segmentation and provide local regions for image retrieval. Second, a local-region image retrieval is conducted to provide a powerful support for the depth estimation of each segmented part. Third, a scene similarity matrix is constructed and combined with the initial depth prior to establish the correlations across different regions for a consistent depth optimization. Experiments show that applying our method to classic data-driven methods can improve the performance of depth estimation. Besides, our results also manifest clearer depth boundaries in some local regions than the state-of-the-art methods based on deep learning framework.

近年来，目睹了单图像深度估计的巨大成功。但是，大多数现有方法仅使用整个图像的场景描述来检索其候选对象，这可能最终导致对局部区域的不良深度支持。本文提出了一种基于数据驱动框架的单图像深度估计分割方法。首先，提出了一种基于像素的边界扩展方法，以改善图像分割并为图像检索提供局部区域。其次，进行局部图像检索，为每个分割部分的深度估计提供有力的支持。第三，在建立跨不同区域的相关性以进行一致的深度优化之前，构建场景相似度矩阵并将其与初始深度合并。实验表明，将我们的方法应用于经典的数据驱动方法可以提高深度估计的性能。此外，与基于深度学习框架的最新方法相比，我们的结果还显示出某些局部区域的深度边界更清晰。