Depth Map Interpolation Using Perceptual Loss

Ilya Makarov, Vladimir Aliev, Olga Gerasimova, Pavel Polyakov

Abstract: In this paper, we discuss a semi-dense depth map interpolation method based on convolutional neural network. We propose a compact neural network architecture with loss function defined as Euclidean distance in the feature space of VGG-16 neural network used for deep visual recognition. The suggested solution shows state-of-art performance on synthetic and real datasets. Together with LSD-SLAM, the method could be used to provide a dense depth map for interaction purposes, such as creating a first person game in AR/MR or perception module for autonomous vehicle.