Layer-Wise Floorplan Extraction for Automatic Urban Building Reconstruction

Urban building reconstruction is an important step for urban digitization and realisticvisualization. In this paper, we propose a novel automatic method to recover urban building geometry from 3D point clouds. The proposed method is suitable for buildings composed of planar polygons and aligned with the gravity direction, which are quite common in the city. Our key observation is that the building shapes are usually piecewise constant along the gravity direction and determined by several dominant shapes. Based on this observation, we formulate building reconstruction as an energy minimization problem under the Markov Random Field (MRF) framework. Specifically, point clouds are first cutinto a sequence of slices along the gravity direction. Then, floorplans are reconstructed by extracting boundaries of these slices, among which dominant floorplans are extracted and propagated to other floors via MRF. To guarantee correct propagation, a new distance measurement for floorplans is designed, which first encodes floorplans into strings and then calculates distances between their corresponding strings. Additionally, an image based editing method is also proposed to recover detailed window structures. Experimental results on both synthetic and real data sets have validated the effectiveness of our method.

1 Introduction

Automatic building reconstruction from point clouds has attracted tremendous attentions from researchers, for its potential applications in fields such as urban planning, virtual reality, civil protection and 3D printing [1]. In last years, 3D data acquisition has experienced great advances ranging from range scans to image based methods. Among various data acquisition techniques, image based methods are the most convenient and commercial way so far. The Bundler algorithm proposed by Snavely et al. [2], followed by the Patched based Multi-view Stereopsis (PMVS) algorithm proposed in [3] is a widely used procedure to generate dense point clouds based on images.