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.

本文提出了一种新颖的方法，用于精确的室内3D重建，通过KinectFusion先建立场景碎片并通过姿态图优化进行注册，然后通过层次凝聚聚类和基于能量的多模型拟合的方式提取场景布局，最后通过全局迭代最近点算法进行点云的全局配准。

室内重建的联合布局估计和全局多视图配准