Point cloud semantic segmentation, which contributes to scene understanding at different
scales, is crucial for three-dimensional reconstruction and digital twin cities. However …

Kernel point convolution (KPConv) can effectively represent the point features of point cloud
data. However, KPConv-based methods just consider the local information of each point …

Automatic point cloud classification (PCC) is a challenging task in large-scale urban point
clouds due to the heterogeneous density of points, the high number of points and the …

This paper proposes EyeNet, a novel semantic segmentation network for point clouds that
addresses the critical yet often overlooked parameter of coverage area size. Inspired by …

Semantic segmentation of airborne laser scanning (ALS) point clouds using deep learning is
a hot research in remote sensing and photogrammetry. A current trend is to aggregate …

In recent years, point clouds have been widely used in power-line inspection, smart cities,
autonomous driving, and other fields. Deep learning-based point cloud processing methods …

For the actual collected point cloud data, there are widespread challenges such as semantic
inconsistency, density variations, and sparse spatial distribution. A network called PointMM …

Completing shapes of point clouds from partial scans is a fundamental problem for 3-D
vision and remote sensing. However, recent methods mainly relied on K-nearest neighbors …

In a point cloud semantic segmentation task, misclassification usually appears on the
semantic boundary. A few studies have taken the boundary into consideration, but they …

Mobile laser scanning (MLS) point clouds have increasingly been a significant data source
for acquiring accurate three-dimensional (3D) semantic information from complex scenes …