Abstract
Efficient building instance segmentation is necessary for many applications such as parallel reconstruction, management and analysis. However, most of the existing instance segmentation methods still suffer from low completeness, low correctness and low quality for building instance segmentation, which are especially obvious for complex building scenes. This paper proposes a novel unsupervised building instance segmentation (UBIS) method of airborne Light Detection and Ranging (LiDAR) point clouds for parallel reconstruction analysis, which combines a clustering algorithm and a novel model consistency evaluation method. The proposed method first divides building point clouds into building instances by the improved kd tree 2D shared nearest neighbor clustering algorithm (Ikd-2DSNN). Then, the geometric feature of the building instance is obtained using the model consistency evaluation method, which is used to determine whether the building instance is a single building instance or a multi-building instance. Finally, for multiple building instances, the improved kd tree 3D shared nearest neighbor clustering algorithm (Ikd-3DSNN) is used to divide multi-building instances again to improve the accuracy of building instance segmentation. Our experimental results demonstrate that the proposed UBIS method obtained good performances for various buildings in different scenes such as high-rise building, podium buildings and a residential area with detached houses. A comparative analysis confirms that the proposed UBIS method performed better than state-of-the-art methods.
Highlights
Light Detection and Ranging (LiDAR) is an active remote sensing system that is less affected by weather conditions such as light and can quickly, in real time, collect threedimensional surface information of the ground or ground objects
The data points obtained by the LiDAR system are dense, accurate, and have three-dimensional location information, so they are regarded as an important data source, for example, for point cloud classification, building instance segmentation and reconstructing three-dimensional building models
We propose a novel unsupervised building instance segmentation (UBIS) method of airborne LiDAR point clouds for parallel reconstruction analysis which combines a clustering algorithm and a model consistency evaluation method
Summary
Light Detection and Ranging (LiDAR) is an active remote sensing system that is less affected by weather conditions such as light and can quickly, in real time, collect threedimensional surface information of the ground or ground objects. There has been more and more research on the instance segmentation of indoor objects or the instance segmentation of cars and people on outdoor roads; their data sources include two-dimensional image data, depth image data and point cloud data, and their processing methods include traditional methods and deep learning methods. Deep learning is a new research tendency in the field of machine learning In recent years, it has been extensively studied such as point cloud semantic segmentation, point cloud classification and point cloud filtering. We propose a novel unsupervised building instance segmentation (UBIS) method of airborne LiDAR point clouds for parallel reconstruction analysis which combines a clustering algorithm and a model consistency evaluation method.
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