Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation

TL;DR

This paper introduces a novel deep learning approach for monocular depth estimation that integrates multi-scale CNN features using continuous CRFs, enabling end-to-end training and achieving state-of-the-art results.

Contribution

It proposes a new method combining multi-scale CNNs with continuous CRFs, including a novel CNN implementation of mean-field updates, for improved depth estimation.

Findings

Achieves state-of-the-art performance on benchmark datasets

Demonstrates effective end-to-end training of the model

Validates the superiority of the proposed CRF-based fusion approach

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.