The Devil is in the Edges: Monocular Depth Estimation with Edge-aware
  Consistency Fusion
Pengzhi Li Yikang Ding Haohan Wang Chengshuai Tang Zhiheng Li
This paper presents a novel monocular depth estimation method, named ECFNet,
for estimating high-quality monocular depth with clear edges and valid overall
structure from a single RGB image. We make a thorough inquiry about the key
factor that affects the edge depth estimation of the MDE networks, and come to
a ratiocination that the edge information itself plays a critical role in
predicting depth details. Driven by this analysis, we propose to explicitly
employ the image edges as input for ECFNet and fuse the initial depths from
different sources to produce the final depth. Specifically, ECFNet first uses a
hybrid edge detection strategy to get the edge map and edge-highlighted image
from the input image, and then leverages a pre-trained MDE network to infer the
initial depths of the aforementioned three images. After that, ECFNet utilizes
a layered fusion module (LFM) to fuse the initial depth, which will be further
updated by a depth consistency module (DCM) to form the final estimation.
Extensive experimental results on public datasets and ablation studies indicate
that our method achieves state-of-the-art performance. Project page:
https://zrealli.github.io/edgedepth.