A framework for reconstructing building parametric models with hierarchical relationships from point clouds

Abstract

Parametric reconstruction based on point clouds of 3D scenes has attracted much attention due to its application prospects in many fields. This paper proposes a novel framework for reconstructing parametric models of buildings with hierarchical relationships from point clouds. Different from traditional approaches focusing on extracting geometric primitives, this work aims to combine the semantic hierarchy of building components with edge measurements based on vertex connections to output parametric models. We show a three-stage automated pipeline for reconstructing parametric models from point clouds of buildings. Regarding to the first stage, we design a deep network architecture to achieve the task of hierarchical segmentation, and propose a metric to evaluate the semantic consistency of different hierarchies. In the second stage, in order to predict the position of corners and filter the correct connections to construct the skeleton-graph of the model, we present a deep network architecture that converts point clouds into skeleton-graph model. The network takes the labeled 3D points output by the semantic hierarchical segmentation network as input, and then outputs the skeleton-graph of the point clouds, which is a set of edge segments connected by corner points. In the third stage, the semantic hierarchical segmentation information of the point clouds is embedded as attributes, and then the geometric parameters are measured according to the edges connected by vertices, and finally the semantic information and geometric features are mapped into the schema of CityGML. We pre-train and validate the reliability of our framework on a finely crafted synthetic dataset and finally we conduct transfer learning and fine-tune the framework on a real scene dataset. Experiments show that our method not only generates high-quality hierarchical parametric models but also recovers clean features and is robust to noise. This study provides practical guidance and technical references for developing more intelligent modeling algorithms that could support data-driven decision-making in smart cities.