Abstract
Three-dimensional (3D) image or video has led human vision to a new generation and brought to the next revolution, because the content, provided for 3D display, is much closer to the real world. Since the device which could capture direct 3D content is of high cost and not common at present compared with 2D cameras, also, the tremendous amount of current and past media data in 2D format should be possible to be viewed with a stereoscopic effect. For these reasons, 2D to 3D conversion becomes a practical and urgent solution to meet the requirement for 3D content providers. Different from traditional two-dimensional (2D) content, depth information is necessary for 3D virtual view generation. Many depth cues can be used to reconstruct 3D information from 2D image. To deal with outdoor scene usually with horizon, we choose relative height cue since it is more general. For an input image, we first use an efficient graph-based image segmentation to compute the segmentation regions. These regions are then used to semantic and surface machine learning system, which gives the main information to estimate the depth map. Second, we identify salient regions that are visually more noticeable to provide the semantic information for the subsequent stages. Horizon detection helps us not only to estimate the depth value but also to enhance semantic and surface classification. Moreover, applying human detection to detect human is a big challenge during semantic labeling stage. Depth estimation based on relative height depth cue is accomplished after these processes.