Urban Building Mesh Polygonization Based on 1-Ring Patch and Topology Optimization

Abstract

With the development of UAV and oblique photogrammetry technology, the multi-view stereo image has become an important data source for 3D urban reconstruction, and the surface meshes generated by it have become a common way to represent the building surface model due to their high geometric similarity and high shape representation ability. However, due to the problem of data quality and lack of building structure information in multi-view stereo image data sources, it is a huge challenge to generate simplified polygonal models from building surface meshes with high data redundancy and fuzzy structural boundaries, along with high time consumption, low accuracy, and poor robustness. In this paper, an improved mesh representation strategy based on 1-ring patches is proposed, and the topology validity is improved on this basis. Experimental results show that our method can reconstruct the concise, manifold, and watertight surface models of different buildings, and it can improve the processing efficiency, parameter adaptability, and model quality.

Iterative Low-Poly Building Model Reconstruction from Mesh Soups Based on Contour

“…[3] focuses on the structure and reconstructs buildings by structure graphs based on planar primitives and their adjacencies, whereas Ref. [12] further uses one-ring patches in place of the original processing unit to improve efficiency. Similarly, Ref.…”

“…For instance, most buildings are primarily composed of planar surfaces. The corresponding planar primitives can be extracted using common primitive-extraction algorithms and, then, used to directly construct the building [8][9][10][11], guide the mesh simplification [5], or reconstruct the building with intermediate representations such as a structure graph [3,12] and a topology graph [13]. Similarly, the contours of buildings have been proven to contain the shape information of the building and have explicit topological relationships between them [14].…”

Multilevel Regularization Method for Building Outlines Extracted from High-Resolution Remote Sensing Images

“…Building outline data have high application value in various fields, such as map creation and updating, disaster analysis, emergency rescue, urban planning, change monitoring, 3D modeling, and population assessment [1]. In recent years, the rapid development of "smart cities," spatial big data, and artificial intelligence has accelerated the demand for diverse, accurate, and timely building outline data [2]. High-resolution remote sensing images contain rich ground object features, including color, size, shape, texture, and spatial relationships between objects [3].…”