GEOP-Net: Shape Reconstruction of Buildings From LiDAR Point Clouds

• Published in: IEEE geoscience and remote sensing letters Vol. 20; p. 1
• Main Authors: Yan, Yiming, Wang, Zilu, Xu, Congan, Su, Nan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Atmospheric modeling; building; Buildings; Feature extraction; Germanium; Image reconstruction; implicit modeling; Laser radar; Lidar; LiDAR point clouds; Point cloud compression; Remote sensing; Shape; shape reconstruction
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2023.3277717

The shape reconstruction of buildings based on LiDAR point clouds is extremely significant in remote sensing. In recent years, reconstruction methods based on the implicit network have been widely used in object-level shape reconstruction. However, the incompleteness and sparsity of airborne LiDAR scanning point clouds will lead to poor reconstruction results. To solve this problem, GEOP-Net: an implicit modeling framework embedded with high-dimensional geometric features, is proposed in this letter. Firstly, the geometric encoding module added to extract high-dimensional features enhances the feature extraction ability of the network to the detailed structures. The point clouds of buildings in Zurich are collected and used to evaluate the performance of the proposed method. The experimental results show that the proposed method have better accuracy than the existing methods, so it provides a new research idea for building reconstruction.