A Transformer-Based Roof Plane Segmentation Approach for Airborne LiDAR Point Clouds

In the fields of photogrammetry and computer vision, 3-D urban building model reconstruction from airborne light detection and ranging (LiDAR) point clouds has attracted significant attention in recent years. Accurately and automatically extracting local geometric structures, such as planar patches, from 3-D point cloud data directly determines the quality of subsequent 3-D model reconstruction. Considering that the roof is a crucial component of a real building, roof plane segmentation is a critical procedure in building 3-D reconstruction. In this article, a novel dual-branch transformer-based network is designed to accurately segment roof planes from airborne LiDAR point clouds. We first use PointNet++ followed with a transformer encoder to extract point-wise feature embeddings. Then, in the first branch, a transformer decoder module is applied to directly learn the instance centers of planar patches by giving a set of learned queries. Because the transformer can effectively model the relations of the queries and the global context information, the instance center positions of all planes included in the input point clouds can be accurately predicted. In this way, the number and center positions of roof planes are known before performing roof plane segmentation. In the second branch, we predict the offsets for each point using its point-wise feature to shift it toward the corresponding instance center. After that, the plane parameters for each plane instance can be estimated using the shifted points around the predicted centers, and the rest of the points are assigned to its nearest plane to generate the final roof planes. The experimental results illustrate that our approach can successfully address the plane segmentation challenge for diverse building roof structures while achieving performance superior to the current state-of-the-art techniques. We will make the source code of our approach publicly available at https://github.com/Li-Li-Whu/PlaneTransformer