In this paper, we focus on recovering a 3-D depth map from a single image via ground-vertical boundary analysis. First, we generate a ground map from the input image based on the spectral matting method, followed by a spatial geometric inference. After that, we derive the depth information for the ground-vertical boundaries. Unlike conventional approaches which generally use plane models to reconstruct a 3-D structure that fits the estimated boundaries, we infer a dense depth map by solving a Maximum-A-Posteriori (MAP) estimation problem. In this MAP problem, we use a generalized spatial-coherence prior model based on the Matting Laplacian (ML) matrix in order to provide a more robust solution for depth inference. We demonstrate that this approach can produce more pleasant depth maps for cluttered scenes.