Automatic Annotation for Semantic Segmentation in Indoor Scenes

Abstract

Domestic robots could eventually transform our lives, but safely operating in home environments requires a rich understanding of indoor scenes. Learning-based techniques for scene segmentation require large-scale, pixel-level annotations, which are laborious and expensive to collect. We propose an automatic method for pixel-wise semantic annotation of video sequences, that gathers cues from object detectors and indoor 3D room-layout estimation and then annotates all the image pixels in an energy minimization framework. Extensive experiments on a publicly available video dataset (SUN3D) evaluate the approach and demonstrate its effectiveness.