Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative
  Convolution Network
Wencheng Han Junbo Yin Jianbing Shen
Monocular depth estimation is known as an ill-posed task in which objects in
a 2D image usually do not contain sufficient information to predict their
depth. Thus, it acts differently from other tasks (e.g., classification and
segmentation) in many ways. In this paper, we find that self-supervised
monocular depth estimation shows a direction sensitivity and environmental
dependency in the feature representation. But the current backbones borrowed
from other tasks pay less attention to handling different types of
environmental information, limiting the overall depth accuracy. To bridge this
gap, we propose a new Direction-aware Cumulative Convolution Network (DaCCN),
which improves the depth feature representation in two aspects. First, we
propose a direction-aware module, which can learn to adjust the feature
extraction in each direction, facilitating the encoding of different types of
information. Secondly, we design a new cumulative convolution to improve the
efficiency for aggregating important environmental information. Experiments
show that our method achieves significant improvements on three widely used
benchmarks, KITTI, Cityscapes, and Make3D, setting a new state-of-the-art
performance on the popular benchmarks with all three types of self-supervision.