Current 3D object detection methods are heavily influenced by 2D detectors. In order to
leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids …

Abstract 3D object detection has seen quick progress thanks to advances in deep learning
on point clouds. A few recent works have even shown state-of-the-art performance with just …

The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-
set registration. However, being based on local iterative optimization, ICP is known to be …

The amount of captured 3D data is continuously increasing, with the democratization of
consumer depth cameras, the development of modern multi‐view stereo capture setups and …

In this paper, we present a robust global approach for point cloud registration from uniformly
sampled points. Based on eigenvalues and normals computed from multiple scales, we …

Gaussian mixture alignment is a family of approaches that are frequently used for robustly
solving the point-set registration problem. However, since they use local optimisation, they …

In this paper, we present an object pose estimation algorithm exploiting both depth and color
information. While many approaches assume that a target region is cleanly segmented from …

Deep networks on 3D point clouds have achieved remarkable success in 3D classification,
while they are vulnerable to geometry variations caused by inconsistent data acquisition …

Widely used in three-dimensional (3D) modeling, reverse engineering and other fields, point
cloud registration aims to find the translation and rotation matrix between two point clouds …

The problem faced in this paper concerns the recognition of simple and complex geometric
primitives in point clouds resulting from scans of mechanical CAD objects. A large number of …