Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring integration of both global and local information from various cues. Moreover, the task is inherently ambiguous, with a large source of uncertainty coming from the overall scale. In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally. We also apply a scale-invariant error to help measure depth relations rather than scale. By leveraging the raw datasets as large sources of training data, our method achieves state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation.
Eigen et al. REF addressed this task by employing two deep network stacks: one is to make a coarse global prediction based on the entire image and the other is to refine this prediction locally, which can combine global and local information from various cues.
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
ArXiv
ArXiv
Computer Science