Due to image noise, image blur, and inconsistency between depth data and color image, the
accuracy and robustness of the pairwise spatial transformation computed by matching …

In this paper, we propose an adaptation of the well-known k-means algorithm for solving the
multiple spheres detection problem when data points are homogeneously scattered around …

Sphere detection in point clouds is an important task in 3D computer vision with various
applications such as reverse engineering, medical imaging, Terrestrial Laser Scans (TLS) …

With the proliferation of cheap sensors and handheld devices, the amount of 3d data has
grown exponentially and finds uses in the automated diagnosis of medical images …

Accurate localization of hedges in 3D space is a key step in automatic pruning. However,
due to the irregularity of the hedge shape, the localization accuracy based on traditional …

Geometric primitives contained in three‐dimensional (3D) point clouds can provide the
meaningful and concise abstraction of 3D data, which plays a vital role in improving 3D …

Most man-made indoor and urban scenes are composed of a set of orthogonal and parallel
planes. In robotics and computer vision, these scenes typically represented by the …

Visual Simultaneous Localization and Mapping (vS-LAM) is essential to autonomous robot
navigation and AR/VR systems. As deep learning technologies improve rapidly, researchers …

Point cloud data classification has been widely used in autonomous driving, robot
perception, and virtual/augmented reality. Due to its irregularity and disorder, the …

Point clouds play a vital role in self-driving vehicle, interactive media and other applications.
However, how to efficiently and robustly extract multiple geometric primitives from point …