  Roof plane segmentation from airborne LiDAR point clouds is an important
technology for 3D building model reconstruction. One of the key issues of plane
segmentation is how to design powerful features that can exactly distinguish
adjacent planar patches. The quality of point feature directly determines the
accuracy of roof plane segmentation. Most of existing approaches use
handcrafted features to extract roof planes. However, the abilities of these
features are relatively low, especially in boundary area. To solve this
problem, we propose a boundary-aware point clustering approach in Euclidean and
embedding spaces constructed by a multi-task deep network for roof plane
segmentation. We design a three-branch network to predict semantic labels,
point offsets and extract deep embedding features. In the first branch, we
classify the input data as non-roof, boundary and plane points. In the second
branch, we predict point offsets for shifting each point toward its respective
instance center. In the third branch, we constrain that points of the same
plane instance should have the similar embeddings. We aim to ensure that points
of the same plane instance are close as much as possible in both Euclidean and
embedding spaces. However, although deep network has strong feature
representative ability, it is still hard to accurately distinguish points near
plane instance boundary. Therefore, we first group plane points into many
clusters in the two spaces, and then we assign the rest boundary points to
their closest clusters to generate final complete roof planes. In this way, we
can effectively reduce the influence of unreliable boundary points. In
addition, we construct a synthetic dataset and a real dataset to train and
evaluate our approach. The experiments results show that the proposed approach
significantly outperforms the existing state-of-the-art approaches.
