As a critical step in 3D scene understanding, semantic segmentation of point clouds has
broad application scenarios, including intelligent driving, augmented reality, smart factories …

Point cloud learning has recently gained strong attention due to its applications in various
fields, like computer vision, robotics, and autonomous driving. Point cloud semantic …

Recent advancements in construction robotics have significantly transformed the
construction industry by delivering safer and more efficient solutions for handling complex …

In this paper, a 3D semantic segmentation method is proposed, in which a novel feature
extraction framework is introduced assembling point initial information embedding (PIIE) and …

With the application of the random sampling method in the down-sampling of point clouds
data, the processing speed of point clouds has been greatly improved. However, the …

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 …

Semantic segmentation of large-scale point clouds is a research hotspot in aerospace
remote sensing technology. Large-scale point clouds face the curse of dimensionality, and …

Semantic segmentation of point clouds at the scene level is a challenging task. Most existing
work relies on expensive sampling techniques and tedious pre-and post-processing steps …

In recent years, the semantic segmentation of 3D point cloud has received increasing
attention the field of computer vision, because 3D point cloud can better reflect our 3D …