MFOC-CliqueNet: A CliqueNet Based Optimal Combination of Multidimensional Features Classification Method for Large-Scale Laser Point Clouds

Abstract As large-scale laser 3D point clouds data contains massive and complex data, it faces great challenges in the automatic intelligent processing and classification of large-scale 3D point clouds. Aiming at the problem that 3D point clouds in complex scenes are self-occluded or occluded, which could affect the object classification accuracy, we propose a multi-dimensional feature optimization combination classification method named MFOC-CliqueNet based on CliqueNet for large-scale laser point clouds. The optimal combination matrix of multi-dimensional features is constructed by extracting the three-dimensional features and multi-directional two-dimensional features of three-dimensional point cloud. This is the first time that multi-dimensional optimal combination features are introduced into cyclic convolutional networks CliqueNet. It is important for large-scale three-dimensional point cloud classification. The experimental results show that the MFOC-CliqueNet framework can realize the latest level with fewer parameters. The classification accuracy of our method is 98.9% on the Large-Scale Scene Point Cloud Oakland dataset, which is better than other classification algorithms mentioned in this paper.