Deep View Synthesis with Compact and Adaptive Multiplane Images
Research Paper / Jan 2022 / Immersive / AR/VR/MR, Light Field, Volumetric Imaging, Machine learning/ Deep learning /Artificial Intelligence
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.