  Multiple near frontal-parallel planes based depth representation demonstrated
impressive results in self-supervised monocular depth estimation (MDE).
Whereas, such a representation would cause the discontinuity of the ground as
it is perpendicular to the frontal-parallel planes, which is detrimental to the
identification of drivable space in autonomous driving. In this paper, we
propose the PlaneDepth, a novel orthogonal planes based presentation, including
vertical planes and ground planes. PlaneDepth estimates the depth distribution
using a Laplacian Mixture Model based on orthogonal planes for an input image.
These planes are used to synthesize a reference view to provide the
self-supervision signal. Further, we find that the widely used resizing and
cropping data augmentation breaks the orthogonality assumptions, leading to
inferior plane predictions. We address this problem by explicitly constructing
the resizing cropping transformation to rectify the predefined planes and
predicted camera pose. Moreover, we propose an augmented self-distillation loss
supervised with a bilateral occlusion mask to boost the robustness of
orthogonal planes representation for occlusions. Thanks to our orthogonal
planes representation, we can extract the ground plane in an unsupervised
manner, which is important for autonomous driving. Extensive experiments on the
KITTI dataset demonstrate the effectiveness and efficiency of our method. The
code is available at https://github.com/svip-lab/PlaneDepth.
