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 …

The iterative closest point (ICP) algorithm and its variants are a fundamental technique for
rigid registration between two point sets, with wide applications in different areas from …

PointNet has recently emerged as a popular representation for unstructured point cloud
data, allowing application of deep learning to tasks such as object detection, segmentation …

We introduce an unsupervised multi-task model to jointly learn point and shape features on
point clouds. We define three unsupervised tasks including clustering, reconstruction, and …

Recent advances in deep convolutional neural networks (CNNs) have motivated
researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local …

This paper focuses on removing mismatches from given putative feature matches created
typically based on descriptor similarity. To achieve this goal, existing attempts usually …

Coherent point drift is a well-known algorithm for solving point set registration problems, ie,
finding corresponding points between shapes represented as point sets. Despite its …

Over the past years, 3D point cloud registration has attracted unprecedented attention.
Researchers develop various approaches to tackle the challenging task, such as …

This paper proposes an effective method, termed as motion-consistency driven matching
(MCDM), for mismatch removal from given tentative correspondences between two feature …

We present a novel, data driven approach for solving the problem of registration of two point
cloud scans. Our approach is direct in the sense that a single pair of corresponding local …