Extracting POP: Pairwise orthogonal planes from point cloud using RANSAC.

• We solve the problem of representation and mapping of non-manifold geometry, appropriately loosen the theoretical restrictions by defining the key parts of the graphics in Sections 3.1 and 3.3. • The approximation method is robust to small planes and damaged data because of our method in Section 3.2. • Stepwise approximation reduce 12 parameters from to 3 parameters at most in each approximation in Section 3.2. Pairwise orthogonal planes are important semantic structures in indoor scenes. The method of Manhattan world orientation calculation and constrained planes selection can solve most segmentation problems of point cloud, but it is inefficient when it comes to small areas on beams, columns or other small architectural primitives. In this paper, we propose a novel method to extract pairwise orthogonal planes (abbr. POP) from point cloud building model. We firstly define the orthogonal structure, and then utilize it as a primitive shape to extract the pairwise orthogonal planes using RANSAC. Our method can segment small regions of beams and columns. Moreover, it can also extract non-manifold structures with broken point cloud data. Experimental results show that our proposed method can extract the POP model efficiently and accurately. [ABSTRACT FROM AUTHOR]