EFFNet urban point cloud classification method

Abstract

Timely and accurate acquisition of urban feature information and urban feature classification from high-precision 3D LiDAR point cloud data has become an international research hotspot. With the rise of deep learning, researchers have gradually considered using deep learning to deal with point cloud classification problems. However, point cloud datasets for urban scenes are different from computer vision datasets in that they have the characteristics of large amounts of data, complex scenes, and abundant category information. Applying deep learning to the point cloud classification problem of urban scenes still has great challenges, and the loss of feature information in the process of network acquisition of multiscale features is the problem that needs to be faced, and these lost features are crucial for point cloud classification of urban scenes. Therefore, from the perspective of local feature information loss, we propose EFFNet (External Feature Fusion Network), which combines end-to-end extracted features and manual descriptors using depth feature and manual descriptor technology to obtain more fine-grained local features of point clouds. Experimental results show that this method has advantages in urban point cloud classification. Therefore, from the perspective of local feature information loss, we propose EFFNet (External Feature Fusion Network), which combines end-to-end extracted features and hand-crafted descriptors using the technology of combining hand-crafted descriptors with depth features to obtain more fine-grained local features of point clouds. Experimental results show that this method has advantages in urban point cloud classification.