  Point cloud classification plays an important role in a wide range of
airborne light detection and ranging (LiDAR) applications, such as topographic
mapping, forest monitoring, power line detection, and road detection. However,
due to the sensor noise, high redundancy, incompleteness, and complexity of
airborne LiDAR systems, point cloud classification is challenging. In this
paper, we proposed a directionally constrained fully convolutional neural
network (D-FCN) that can take the original 3D coordinates and LiDAR intensity
as input; thus, it can directly apply to unstructured 3D point clouds for
semantic labeling. Specifically, we first introduce a novel directionally
constrained point convolution (D-Conv) module to extract locally representative
features of 3D point sets from the projected 2D receptive fields. To make full
use of the orientation information of neighborhood points, the proposed D-Conv
module performs convolution in an orientation-aware manner by using a
directionally constrained nearest neighborhood search. Then, we designed a
multiscale fully convolutional neural network with downsampling and upsampling
blocks to enable multiscale point feature learning. The proposed D-FCN model
can therefore process input point cloud with arbitrary sizes and directly
predict the semantic labels for all the input points in an end-to-end manner.
Without involving additional geometry features as input, the proposed method
has demonstrated superior performance on the International Society for
Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark dataset. The
results show that our model has achieved a new state-of-the-art level of
performance with an average F1 score of 70.7%, and it has improved the
performance by a large margin on categories with a small number of points (such
as powerline, car, and facade).
