Point clouds are increasingly being used to improve productivity, quality, and safety
throughout the life cycle of construction and infrastructure projects. While applicable for …

Airborne light detection and ranging (LiDAR) has now become industry standard tool for
collecting accurate and dense topographic data at very high speed. These data have found …

Large-scale 3D point clouds are often used as training data for 3D semantic segmentation,
but the labor-intensive nature of the annotation process challenges the acquisition of …

Charts are an essential part of both graphicacy (graphical literacy), and statistical literacy. As
chart understanding has become increasingly relevant in data science, automating chart …

Power corridor classification using LiDAR (light detection and ranging) point clouds is an
important means for power line inspection. Many supervised classification methods have …

Topographic Light Detection and Ranging (LiDAR) captures geometric information of the
topography of a geographical region, often using airborne platforms. The research and …

Point classification is necessary for detection and extraction of geometric feature (folds,
creases, junctions, surfaces), and subsequent 3D reconstruction of point-sampled geometry …

Road surface geometry provides information about navigable space in autonomous driving.
Ground plane estimation is done on “road” points after semantic segmentation of three …

Airborne LiDAR datasets, in the form of three-dimensional point clouds, provide geometric
information, owing to their spatial nature. Their popularity as a geospatial data acquisition …

Navigable space determination is a difficult problem encountered in robotics and intelligent
vehicle technology and it requires integrated solutions using advances in computer vision …