Abstract
Three-dimensional (3D) building models play an important role in digital cities and have numerous potential applications in environmental studies. In recent years, the photogrammetric point clouds obtained by aerial oblique images have become a major source of data for 3D building reconstruction. Aiming at reconstructing a 3D building model at Level of Detail (LoD) 2 and even LoD3 with preferred geometry accuracy and affordable computation expense, in this paper, we propose a novel method for the efficient reconstruction of building models from the photogrammetric point clouds which combines the rule-based and the hypothesis-based method using a two-stage topological recovery process. Given the point clouds of a single building, planar primitives and their corresponding boundaries are extracted and regularized to obtain abstracted building counters. In the first stage, we take advantage of the regularity and adjacency of the building counters to recover parts of the topological relationships between different primitives. Three constraints, namely pairwise constraint, triplet constraint, and nearby constraint, are utilized to form an initial reconstruction with candidate faces in ambiguous areas. In the second stage, the topologies in ambiguous areas are removed and reconstructed by solving an integer linear optimization problem based on the initial constraints while considering data fitting degree. Experiments using real datasets reveal that compared with state-of-the-art methods, the proposed method can efficiently reconstruct 3D building models in seconds with the geometry accuracy in decimeter level.
Highlights
Starting from the photogrammetric point clouds of aerial oblique images for single buildings, Figure 1 shows the overall workflow of the proposed method
In the pre-processing steps, the planar primitives in the point clouds are extracted with simple parallel and orthogonal constraints using the existing RANSAC-based methods
We proposed a novel method for efficient reconstructing building models from photogrammetric point clouds obtained from aerial oblique images in a two-stage topology recovery process which combined the rule-based and the hypothesis-based methods
Summary
Three-dimensional (3D) building models play an important role in constructing digital cities and have numerous environmental applications in areas such as urban planning [1], smart city [2], environmental analysis [3], and other civil engineering [4]. With the rapid development of aerial vehicles, cameras, and image-processing technologies, aerial oblique images have become a major data source for 3D city modeling [5,6]. Due to the time-consuming and labor-intensive nature of manual modeling processes, researchers in the photogrammetry, computer vision, and graphic communities have developed automatic building model reconstruction methods [7,8,9,10]. Due to the presence of occlusions in Remote Sens.
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