Deep Convolutional Priors for Indoor Scene Synthesis

Kai Wang, Manolis Savva, Angel X. Chang, Daniel Ritchie

Abstract:

We present a convolutional neural network based approach for indoor scene
synthesis. By representing 3D scenes with a semantically-enriched image based representation based on orthographic top-down views, we learn convolutional object placement priors from the entire context of a room. Our
approach iteratively generates rooms from scratch, given only the room
architecture as input. Through a series of perceptual studies we compare
the plausibility of scenes generated using our method against baselines for
object selection and object arrangement, as well as scenes modeled by people. We find that our method generates scenes that are preferred over the
baselines, and in some cases are equally preferred to human-created scenes