Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
This work addresses depth estimation from single images, an incremental improvement for computer vision applications.
The paper tackles monocular depth estimation by fusing multi-scale CNN outputs using continuous Conditional Random Fields (CRFs), implemented as sequential deep networks for end-to-end training, achieving new state-of-the-art results on public datasets.
This paper addresses the problem of depth estimation from a single still image. Inspired by recent works on multi- scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through extensive experimental evaluation we demonstrate the effective- ness of the proposed approach and establish new state of the art results on publicly available datasets.