  By moving a depth sensor around a room, we compute a 3D CAD model of the
environment, capturing the room shape and contents such as chairs, desks,
sofas, and tables. Rather than reconstructing geometry, we match, place, and
align each object in the scene to thousands of CAD models of objects. In
addition to the fully automatic system, the key technical contribution is a
novel approach for aligning CAD models to 3D scans, based on deep reinforcement
learning. This approach, which we call Learning-based ICP, outperforms prior
ICP methods in the literature, by learning the best points to match and
conditioning on object viewpoint. LICP learns to align using only synthetic
data and does not require ground truth annotation of object pose or keypoint
pair matching in real scene scans. While LICP is trained on synthetic data and
without 3D real scene annotations, it outperforms both learned local deep
feature matching and geometric based alignment methods in real scenes. The
proposed method is evaluated on real scenes datasets of SceneNN and ScanNet as
well as synthetic scenes of SUNCG. High quality results are demonstrated on a
range of real world scenes, with robustness to clutter, viewpoint, and
occlusion.
