Consistent correspondences between point clouds are vital to 3D vision tasks such as
registration and recognition. In this paper, we present a mutual voting method for ranking 3D …

Generally, performing multiple scans is necessary to cover entire scanning area, and
multiple point clouds are thus obtained. These point clouds need to be registered together …

Point cloud registration is a fundamental problem in many applications. The point cloud
registration based on local shape descriptor has been widely researched. In order to further …

Local feature description is the fundamental research topic for 3D rigid data matching.
However, how to achieve a good balanced performance of the local shape descriptor …

Recognizing 3D objects based on local feature descriptors, in point cloud scenes with
occlusion and clutter, is a very challenging task. Most existing 3D local feature descriptors …

Registration is usually the first step for the usage of point cloud data. Registration of point
clouds is crucial in numerous practical applications. In order to quickly and accurately align …

Abstract 3D Local feature description is an active and fundamental task in 3D computer
vision. However, most of the existing descriptors fail to simultaneously achieve satisfactory …

Recognizing objects in dense clutter accurately plays an important role to a wide variety of
robotic manipulation tasks including grasping, packing, rearranging and many others …

Point cloud registration based on local descriptors plays a crucial role in 3D computer vision
applications. However, existing methods often suffer from limitations such as low accuracy, a …

Abstract Three-dimensional (3D) local feature descriptor plays an important role in 3D
computer vision because it is widely used to build point-to-point correspondences in many …