Reconstruction-based Pairwise Depth Dataset for
Depth Image Enhancement Using CNN
Abstract. Raw depth images captured by consumer depth cameras suffer from noisy and missing values. Despite the success of CNN-based image processing on color image restoration, similar approaches for depth
enhancement have not been much addressed yet because of the lack of
raw-clean pairwise dataset. In this paper, we propose a pairwise depth
image dataset generation method using dense 3D surface reconstruction
with a filtering method to remove low quality pairs. We also present a
multi-scale Laplacian pyramid based neural network and structure preserving loss functions to progressively reduce the noise and holes from
coarse to fine scales. Experimental results show that our network trained
with our pairwise dataset can enhance the input depth images to become
comparable with 3D reconstructions obtained from depth streams, and
can accelerate the convergence of dense 3D reconstruction results