A Learned Joint Depth and Intensity Prior Using Markov Random Fields
Cardenal Herrera, Juho Kannala, Peter F. Sturm, Janne Heikkilä · 2013
We present a joint prior that takes intensity and depth information into account. The prior is defined using a flexible Field-of-Experts model and is learned from a dataBase of natural images. It is a generative model and has an efficient method for sampling. We use sampling from the model to perform in painting and up sampling of depth maps when intensity information is available. We show that including the intensity information in the prior improves the results obtained from the model. We also compare to another two-channel in painting approach and show superior results.