Real-time Light Estimation and Neural Soft Shadows for AR Indoor
  Scenarios
Alexander Sommer Ulrich Schwanecke Elmar Schömer
We present a pipeline for realistic embedding of virtual objects into footage
of indoor scenes with focus on real-time AR applications. Our pipeline consists
of two main components: A light estimator and a neural soft shadow texture
generator. Our light estimation is based on deep neural nets and determines the
main light direction, light color, ambient color and an opacity parameter for
the shadow texture. Our neural soft shadow method encodes object-based
realistic soft shadows as light direction dependent textures in a small MLP. We
show that our pipeline can be used to integrate objects into AR scenes in a new
level of realism in real-time. Our models are small enough to run on current
mobile devices. We achieve runtimes of 9ms for light estimation and 5ms for
neural shadows on an iPhone 11 Pro.