Two-dimensional (2D) image processing and three-dimensional (3D) LIDAR point cloud
data analytics are two important techniques of sensor data processing for many applications
such as autonomous systems, auto driving cars, medical imaging and many other fields.
However, 2D image data are the data that are distributed in regular 2D grids while
3D LIDAR data are represented in point cloud format that consist of points nonuniformly
distributed in 3D spaces. Their different data representations lead to different data
processing techniques. Usually, the irregular structures of 3D LIDAR data often cause
challenges of 3D LIDAR analytics. Thus, very successful diffusion equation methods
for image processing are not able to apply to 3D LIDAR processing. In this paper,
applying network and network dynamics theory to 2D images and 3D LIDAR analytics,
we propose graph-based data processing techniques that unify 2D image processing and
3D LIDAR data analytics. We demonstrate that both 2D images and 3D point cloud data
can be processed in the same framework, and the only difference is the way to choose
neighbor nodes. Thus, the diffusion equation techniques in 2D image processing can
be used to process 3D point cloud data. With this general framework, we propose a
new adaptive diffusion equation technique for data processing and show with experiments
that this new technique can perform data processing with high performance.
