D-Net: A Density-Based Convolutional Neural Network for Mobile LiDAR Point Clouds Classification in Urban Areas

Abstract

The 3D semantic segmentation of a LiDAR point cloud is essential for various complex infrastructure analyses such as roadway monitoring, digital twin, or even smart city development. Different geometric and radiometric descriptors or diverse combinations of point descriptors can extract objects from LiDAR data through classification. However, the irregular structure of the point cloud is a typical descriptor learning problem—how to consider each point and its surroundings in an appropriate structure for descriptor extraction? In recent years, convolutional neural networks (CNNs) have received much attention for automatic segmentation and classification. Previous studies demonstrated deep learning models’ high potential and robust performance for classifying complicated point clouds and permutation invariance. Nevertheless, such algorithms still extract descriptors from independent points without investigating the deep descriptor relationship between the center point and its neighbors. This paper proposes a robust and efficient CNN-based framework named D-Net for automatically classifying a mobile laser scanning (MLS) point cloud in urban areas. Initially, the point cloud is converted into a regular voxelized structure during a preprocessing step. This helps to overcome the challenge of irregularity and inhomogeneity. A density value is assigned to each voxel that describes the point distribution within the voxel’s location. Then, by training the designed CNN classifier, each point will receive the label of its corresponding voxel. The performance of the proposed D-Net method was tested using a point cloud dataset in an urban area. Our results demonstrated a relatively high level of performance with an overall accuracy (OA) of about 98% and precision, recall, and F1 scores of over 92%.

IPCONV: Convolution with Multiple Different Kernels for Point Cloud Semantic Segmentation

“…Of the early adopters, VoxNet [22] utilized voxelization to handle point clouds, yet memory constraints posed a challenge. Consequently, various networks have emerged to enhance its computational efficiency and model efficacy [23][24][25][26][27][28][29]. In the pursuit of optimizing 3D data memory utilization, ingenious data structure algorithms have been harnessed to translate data into a sparse framework.…”

“…To tackle the intricate task of learning from sparsely distributed points and encapsulating contextual details, SVASeg [28] has harnessed attention mechanisms. D-Net [29] has introduced an innovative density-based voxelization procedure to enhance the situation on a parallel front. This approach entails assigning each voxel a density value that properly characterizes the distribution of points within its designated location.…”

AFRNet: Anchor-Free Object Detection Using Roadside LiDAR in Urban Scenes

“…In recent years, with the maturity of LiDAR technology and the miniaturization of sensors, LiDAR has been gradually applied to urban remote sensing [1,2] fields such as urban semantic segmentation [3,4], urban scene classification [5,6], reconstruction of urban models [7,8], and object detection [9,10]. The 3D point cloud collected by LiDAR not only visualizes the actual size and shape structure of a 3D object more intuitively, but also provides spatial information such as the 3D position and orientation of objects, which allows us to better understand a 3D scene.…”