Removing bounding surfaces such as walls, windows, curtains, and floor (ie, super-
surfaces) from a point cloud is a common task in a wide variety of computer vision …

Scanning everyday objects with depth sensors is the state-of-the-art approach to generating
point clouds for realistic 3D representations. However, the resulting point cloud data suffers …

Plane segmentation is a basic task in the automatic reconstruction of indoor and urban
environments from unorganized point clouds acquired by laser scanners. As one of the most …

In a large-scale epidemic, reducing direct contact among medical personnel, attendants and
patients has become a necessary means of epidemic prevention and control. Intelligent …

This letter presents a real-time online vision framework to jointly recover an indoor scene's
3D structure and semantic label. Given noisy depth maps, a camera trajectory, and 2D …

Recently, three-dimensional (3D) laser scanning technology has gradually become a main
method of retrieving geometric information of objects and scenes. By processing the point …

Supervoxels provide a natural and compact representation of 3D point clouds that enables
operations to be performed on regions rather than on scattered points. However, most …

Raw depth maps captured by depth sensors generally contain missing contents due to
glossy, transparent, and sparsity problems. Recent methods well completed flat regions of …

Traditional indoor 3D structural environment modeling algorithms employ schemes such as
clustering of dense point clouds for parameterization and identification of the 3D surfaces …

Indoor plane extraction on point cloud has always been a research hotspot, in which random
sample consensus (RANSAC) is known as a common algorithm. However, impacted by …