  In this paper, we propose a novel method to jointly solve scene layout
estimation and global registration problems for accurate indoor 3D
reconstruction. Given a sequence of range data, we first build a set of scene
fragments using KinectFusion and register them through pose graph optimization.
Afterwards, we alternate between layout estimation and layout-based global
registration processes in iterative fashion to complement each other. We
extract the scene layout through hierarchical agglomerative clustering and
energy-based multi-model fitting in consideration of noisy measurements. Having
the estimated scene layout in one hand, we register all the range data through
the global iterative closest point algorithm where the positions of 3D points
that belong to the layout such as walls and a ceiling are constrained to be
close to the layout. We experimentally verify the proposed method with the
publicly available synthetic and real-world datasets in both quantitative and
qualitative ways.
