Humans have an amazing ability to perceive depth from a single still image; however, it remains a challenging problem for current computer vision systems. In this paper, we will present algorithms for estimating depth from a single still image. There are numerous monocular cues—such as texture variations and gradients, defocus, color/haze, etc.—that can be used for depth perception. Taking a supervised learning approach to this problem, in which we begin by collecting a training set of single images and their corresponding groundtruth depths, we learn the mapping from image features to the depths. We then apply these ideas to create 3-d models that are visually-pleasing as well as quantitatively accurate from individual images. We also discuss applications of our depth perception algorithm in robotic navigation, in improving the performance of stereovision, and in creating large-scale 3-d models given only a small number of images.