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Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis by Zhang, Yongjun (author);Yang, Wangshan (author);Liu, Xinyi (author);Wan, Yi (author);Zhu, Xianzhang (author);Tan, Yuhui (author) is a Environmental Science article available to read on EtoBox.

What is Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis about?

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 imp

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Author

Zhang, Yongjun (author);Yang, Wangshan (author);Liu, Xinyi (author);Wan, Yi (author);Zhu, Xianzhang (author);Tan, Yuhui (author)

Publisher

MDPI AG

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)