SliceNet: Deep Dense Depth Estimation From a Single Indoor Panorama Using a Slice-Based Representation

We introduce a novel deep neural network to estimate a depth map from a single monocular indoor panorama. The network directly works on the equirectangular projection, exploiting the properties of indoor 360 • images. Starting from the fact that gravity plays an important role in the design and construction of man-made indoor scenes, we propose a compact representation of the scene into vertical slices of the sphere, and we exploit long-and short-term relationships among slices to recover the equirectangular depth map. Our design makes it possible to maintain highresolution information in the extracted features even with a deep network. The experimental results demonstrate that our method outperforms current state-of-the-art solutions in prediction accuracy, particularly for real-world data.