FLOOR DETECTION BASED DEPTH ESTIMATION FROM A SINGLE INDOOR SCENE

Estimating depth information from a single image has recently attracted great attention in various vision-based applications such as mobile robot navigation. Although there are numerous depth map generation methods, little effort has been done on the depth estimation from a single indoor scene. In this paper, we propose a novel method for estimating depth from a single indoor image via nonlinear diffusion and image segmentation techniques. One important advantage of our approach is that no learning scheme is required to estimate a depth map. Based on the proposed method, we obtain visually plausible depth estimation results even with the presence of occlusions or clutters in the single indoor image. From experimental results, we confirm that the proposed algorithm provides reliable depth information under various indoor environments.