A method for estimating depth map on a single outdoor image using scene classification and pictorial depth cues

Abstract

Estimating depth information from a single image has recently attracted great attention in 3D-TV applications such as 2D-to-3D conversion owing to an insufficient supply of 3D contents. In this paper, we present a new framework for estimating depth from a single image via scene classification techniques. Our goal is to produce "perceptually reasonable" depth for human viewers; we refer to this as "pesudo depth estimation". Since human visual system (HVS) highly relies on structural information and salient objects in understanding scenes, we propose a framework that combines two depth maps; initial pseudo depth map (PDM) and focus depth map (FDM). We use machine learning based scene classification to classify the image into one of two classes, namely object-view and non-object-view. The initial PDM is estimated by segmenting salient objects (in case of object-view) and analyzing scene structure (in case of non-object-view). The focus blur is locally measured to improve the initial PDM. Two depth maps are combined and a simple filtering method is employed to generate the final PDM. Simulation results show that the proposed method outperforms other state-of-the-art approaches for depth estimation in 2D-to-3D conversion both quantitatively and qualitatively. Furthermore, we discuss how the proposed method can be effectively extended to image sequences.