摘要
We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption.The method first aligns the point cloud with a per-building local coordinate system,and then fits axis-aligned planes to the point cloud through an iterative regularization process.The refined planes partition the space of the data into a series of compact cubic cells(candidate boxes)spanning the entire 3D space of the input data.We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation.The objective function is designed to maximize the point cloud coverage and the compactness of the final model.Finally,all selected boxes are merged into a lightweight polygonal mesh model,which is suitable for interactive visualization of large scale urban scenes.Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.
