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 high-resolution 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.

Automated summary

The study introduces a novel deep neural network for estimating depth maps from single monocular indoor panoramas. By compactly representing the scene into vertical slices of the sphere and exploiting relationships among slices, the network is able to recover the equirectangular depth map. Experimental results demonstrate that the method outperforms current solutions in prediction accuracy.