This paper presents a comprehensive survey on vision-based robotic grasping. We
conclude three key tasks during vision-based robotic grasping, which are object localization …

This study had two main aims:(1) to provide a comprehensive review of terrestrial laser
scanner (TLS) point cloud registration methods and a better understanding of their strengths …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Extracting robust and general 3D local features is key to downstream tasks such as point
cloud registration and reconstruction. Existing learning-based local descriptors are either …

Abstract Iterative Closest Point (ICP) solves the rigid point cloud registration problem
iteratively in two steps:(1) make hard assignments of spatially closest point …

Extracting geometric features from 3D scans or point clouds is the first step in applications
such as registration, reconstruction, and tracking. State-of-the-art methods require …

A successful point cloud registration often lies on robust establishment of sparse matches
through discriminative 3D local features. Despite the fast evolution of learning-based 3D …

We propose 3DSmoothNet, a full workflow to match 3D point clouds with a siamese deep
learning architecture and fully convolutional layers using a voxelized smoothed density …

Abstract We present PPFNet-Point Pair Feature NETwork for deeply learning a globally
informed 3D local feature descriptor to find correspondences in unorganized point clouds …

We present a new method for estimating the 6D pose of rigid objects with available 3D
models from a single RGB input image. The method is applicable to a broad range of …