In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit
point density variations. Some of these density variations indicate issues with point clouds …

Light detection and ranging (LiDAR)-derived point cloud has become the standard spatial
data for digital terrain model (DTM) construction; however, it suffers from huge data with …

The recent advances in 3D sensing technology have made possible the capture of point
clouds in significantly high resolution. However, increased detail usually comes at the …

This paper proposes a simplification algorithm based on four feature parameters, aiming at
solving the problem that the edge features cannot be retained due to the incompletely …

While the reconstruction of 3D objects is increasingly used today, the simplification of 3D
point cloud, however, becomes a substantial phase in this process of reconstruction. This is …

High-resolution 3D scanning devices produce high-density point clouds, which require a
large capacity of storage and time-consuming processing algorithms. In order to reduce both …

With the development of 3D sensing technologies, point clouds have attracted increasing
attention in a variety of applications for 3D object representation, such as autonomous …

LiDAR technology has been widely used in forest survey and research, but the high-
resolution point cloud data generated by LiDAR equipment also pose challenges in storage …

With the development of 3D scanning technology, a huge volume of point cloud data has
been collected at a lower cost. The huge data set is the main burden during the data …

Deep convolutional neural network used for image classification is an important part of deep
learning and has great significance in the field of computer vision. Moreover, it helps …