Title: SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes,
Abstract: This paper presents SceneCut, a novel approach to jointly discover previously
unseen objects and non-object surfaces using a single RGB-D image. SceneCut's
joint reasoning over scene semantics and geometry allows a robot to detect and
segment object instances in complex scenes where modern deep learning-based
methods either fail to separate object instances, or fail to detect objects
that were not seen during training. SceneCut automatically decomposes a scene
into meaningful regions which either represent objects or scene surfaces. The
decomposition is qualified by an unified energy function over objectness and
geometric fitting. We show how this energy function can be optimized
efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage
a pre-trained convolutional oriented boundary network to predict accurate
boundaries from images, which are used to construct high-quality region
hierarchies. We evaluate SceneCut on several different indoor environments, and
the results show that SceneCut significantly outperforms all the existing
methods.