This paper proposes EyeNet, a novel semantic segmentation network for point clouds that
addresses the critical yet often overlooked parameter of coverage area size. Inspired by …

In the field of deep point cloud understanding KPConv is a unique architecture that uses
kernel points to locate convolutional weights in space instead of relying on Multi-Layer …

The ability to construct concise scene representations from sensor input is central to the field
of robotics. This paper addresses the problem of robustly creating a 3D representation of a …

At the present time, many publicly available point cloud datasets exist, which are mainly
focused on autonomous driving. The objective of this study is to develop a new large-scale …

Elaboration of topographic maps from Mobile Laser Scanner (MLS) point clouds is a long
and tedious manual task. This study presents a method that automates the underlying …

3D object recognition is one of the most popular areas of study in computer vision. Many of
the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects …

Rover navigation on planetary surfaces currently uses a method called blind drive which
requires a navigation goal as input from operators on Earth and uses camera images to …