  In this paper, we propose a self-supervised single-view pixel-level accurate
depth estimation network, called PLADE-Net. The PLADE-Net is the first work
that shows unprecedented accuracy levels, exceeding 95\% in terms of the
$\delta^1$ metric on the challenging KITTI dataset. Our PLADE-Net is based on a
new network architecture with neural positional encoding and a novel loss
function that borrows from the closed-form solution of the matting Laplacian to
learn pixel-level accurate depth estimation from stereo images. Neural
positional encoding allows our PLADE-Net to obtain more consistent depth
estimates by letting the network reason about location-specific image
properties such as lens and projection distortions. Our novel distilled matting
Laplacian loss allows our network to predict sharp depths at object boundaries
and more consistent depths in highly homogeneous regions. Our proposed method
outperforms all previous self-supervised single-view depth estimation methods
by a large margin on the challenging KITTI dataset, with unprecedented levels
of accuracy. Furthermore, our PLADE-Net, naively extended for stereo inputs,
outperforms the most recent self-supervised stereo methods, even without any
advanced blocks like 1D correlations, 3D convolutions, or spatial pyramid
pooling. We present extensive ablation studies and experiments that support our
method's effectiveness on the KITTI, CityScapes, and Make3D datasets.
