Point cloud registration is a research field where the spatial relationship between two or
more sets of points in space is determined. Point clouds are found in multiple applications …

How to learn effective features from large-scale point clouds for semantic segmentation has
attracted increasing attention in recent years. Addressing this problem, we propose a …

Point cloud segmentation is fundamental in understanding 3D environments. However,
current 3D point cloud segmentation methods usually perform poorly on scene boundaries …

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

Labelling point clouds fully is highly time-consuming and costly. As larger point cloud
datasets with billions of points become more common, we ask whether the full annotation is …

Three-dimensional reconstruction technology has demonstrated broad application potential
in the industrial, construction, medical, forestry, agricultural, and pastural sectors in the last …

Given the prominence of 3D sensors in recent years, 3D point cloud scene data are worthy
to be further investigated. Point cloud scene understanding is a challenging task because of …

Recent state-of-the-art methods for point cloud semantic segmentation are based on
convolution defined for point clouds The interest goes beyond semantic segmentation. We …

Semantic context plays a significant role in image segmentation. However, few prior works
have explored semantic contexts for 3D point cloud segmentation. In this paper, we propose …

How to learn long-range dependencies from 3D point clouds is a challenging problem in 3D
point cloud analysis. Addressing this problem, we propose a global attention network for …