  Recently, learning methods have been designed to create Multiplane Images
(MPIs) for view synthesis. While MPIs are extremely powerful and facilitate
high quality renderings, a great amount of memory is required, making them
impractical for many applications. In this paper, we propose a learning method
that optimizes the available memory to render compact and adaptive MPIs. Our
MPIs avoid redundant information and take into account the scene geometry to
determine the depth sampling.
