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Sparse Depth Map Interpolation using Deep Convolutional Neural Networks

P. 1-5.
Makarov I., Alisa Korinevskaya, Vladimir Aliev.

The problem of dense depth map inference from sparse depth values is considered in this paper. We address this issue in situation when one has low-cost sensor data and limited computational resources. We propose a method that performs interpolation and then super-resolution while comparing our approach with the state-of-the-art direct RGB-to-Dense reconstruction solutions. In particular, we use an encoder-decoder model of CNN with loss consisting of standard mean squared error and perceptual loss function. Futhermore, it has been shown that the described approach could be adopted to estimate rough depth map in real-time.