RANSAC-based multi primitive building reconstruction from 3D point clouds

Building model reconstruction from 3D point clouds has been investigated for several decades with increasing interests. Building models represented by one or more parametric primitives can assure regularity and provide semantic information for the reconstructed buildings. However, there exist challenges in reliably determining building primitives, especially for compound buildings with multiple primitives. This paper presents a multi primitive reconstruction (MPR) approach to segment a compound bounding into several predefined primitives and determine their parameters from the point clouds. The method consists of primitive segmentation through a two-step RANSAC strategy, followed by holistic primitive fitting, and 3D Boolean operations. The first step segments the point cloud of a building into planar patches. The second step applies RANSAC strategy to further segment the predefined building primitives, where only points on adjacent planar patches are sampled to achieve high computational efficiency. The most probable primitive is then selected based on a set of quality metrics and corresponding parameters are holistically estimated with the identified inliers to form the building model. Finally, the 3D Boolean operation is used to reconstruct a topologically consistent 3D building model from its compositional primitives. The proposed RANSAC-MPR method has following advantages. (1) The framework for primitive segmentation is efficient since the sampling only occurs to adjacent planar patches; (2) the type of building primitives can be identified based on a score function without using advanced learning process; (3) compound buildings can be reconstructed through 3D union of the primitives determined by holistic fitting. Tested with 1054 buildings in three lidar and photogrammetry point clouds, the development is able to produce compound building models with regularized primitives at 85% boundary consistency and overall accuracy of 7 cm, which is about 0.14 times and 0.56 times ground point spacing for the lidar and photogrammetry datasets, respectively.