Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

The concept of symmetry has received significant attention in computer graphics and
computer vision research in recent years. Numerous methods have been proposed to find …

Abstract We present Lepard, a Learning based approach for partial point cloud matching in
rigid and deformable scenes. The key characteristics are the following techniques that …

We present a simple, flexible, and general framework titled Partial Registration Network
(PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning …

Three-dimensional surface registration transforms multiple three-dimensional data sets into
the same coordinate system so as to align overlapping components of these sets. Recent …

We present a novel representation of maps between pairs of shapes that allows for efficient
inference and manipulation. Key to our approach is a generalization of the notion of map …

Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in
computer vision. Traditional learning-based methods designed to learn end-to-end 3D flow …

We review methods designed to compute correspondences between geometric shapes
represented by triangle meshes, contours or point sets. This survey is motivated in part by …

Depth camera such as Microsoft Kinect, is much cheaper than conventional 3D scanning
devices, and thus it can be acquired for everyday users easily. However, the depth data …

We study the problem of outlier correspondence pruning for non-rigid point cloud
registration. In rigid registration, spatial consistency has been a commonly used criterion to …