Monocular depth estimation of outdoor scenes using RGB-D datasets

Abstract

Depth estimation is a classical topic in computer vision, however, inferring the depth of a scene from a single image remains an extremely difficult problem. In this paper, a non-parametric method is adopted to obtain the depth of a single image. To this end, RGB-D datasets are exploited as the inference basis. Given a query image, a global scene-level retrieval is performed against the dataset, followed by a superpixel-level matching. The superpixels-based scene representation is introduced to model the depth jointly in terms of superpixel centroid. The depth estimation is formulated as contextual inference and the depth propagation. The contextual inference is expressed as a Markov random field (MRF) energy function defined on a sparse depth map obtained by the matching process and implemented in a graphical model whose edges encode the interactions between the superpixel centroids. Then the depth propagation generates the final dense depth map from the inferred result. The benefits of the proposed method is demonstrated on the standard dataset.