3D scene understanding is important for many applications in the computer vision field. However, the majority of existing solutions commonly assume the images to be captured in clear media. In real world situations, we may encounter less than ideal conditions, for example haze or fog. In these cases, the captured images will contain scattering and veiling effects that obscure the features of the scene. Many studies approach these images by first removing the scattering effects to obtain an approximate clear image. However, by studying the physical model of light propagation in scattering media, we have observed a relation between the captured image intensity and the distance from the camera. Therefore, as a contrast, we attempt to exploit these scattering effects to obtain 3D depth cues. In order to learn the relation between the scattering effects and the depth, we utilize deep networks to help extract and build high-level features. In this paper, we propose a novel classification approach for depth map estimation of hazy images using deep learning.