Title: Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation,
Abstract: 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.