The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …

Manual annotation of every point in a point cloud is a costly and labor-intensive process.
While weakly supervised point cloud semantic segmentation (WSPCSS) with sparse …

With the rapid development of LiDAR and artificial intelligence technologies, 3D point cloud
semantic segmentation has become a highlight research topic. This technology is able to …

The costly and time-consuming annotation process to produce large training sets for
modelling semantic LiDAR segmentation methods has motivated the development of semi …

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

Semi-supervised learning (SSL) plays a crucial role in airborne laser scanning (ALS) point
cloud semantic segmentation to reduce the cost of sample labeling. However, the prevailing …

Point clouds, a prominent method of 3D representation, are extensively utilized across
industries such as autonomous driving, surveying, electricity, architecture, and gaming, and …

Current methods for point cloud semantic segmentation depend on the extraction of
descriptive features. However, unlike images, point clouds are irregular and often lack …

Large-scale datasets with point-wise semantic and instance labels are crucial to 3D instance
segmentation but also expensive. To leverage unlabeled data, previous semi-supervised 3D …