Template-based point cloud modeling for building model

We introduce a novel template-based modeling technique for 3D point clouds sampled from unknown buildings. The approach is based on a hierarchy algebraic template to fit noisy point clouds with sharp features. In the hierarchy template, the first-level, i.e., the lowest-level, contains three kinds of primitive geometries: plane, sphere, and cylinder. These primitive geometries are represented in algebra form. In the higher levels, some simple shapes are constructed by combining these primitive geometries, and the simple shapes can are further joined to form the final template model. In the fitting process, different to the general approaches which are intrinsic an iterative fitting process, we fit point clouds by directly solving a least-square linear system. This makes the approach efficient and robust in the point cloud modeling. Furthermore, some geometric constraints are taken into account in the fitting process for the purpose of increasing modeling accuracy. The experiment results show that the modeling accuracy is improved by integrating the geometric constraints in the fitting process, and the proposed template-based fitting is robust, in terms of withstanding noises and preserving sharp features, than the approaches based on implicit surfaces.