Abstract We present Deep Global Registration, a differentiable framework for pairwise
registration of real-world 3D scans. Deep global registration is based on three modules: a 6 …

Pruning is one of the most important tree fruit production activities, which is highly
dependent on human labor. Skilled labor is in short supply, and the increasing cost of labor …

A point cloud as a collection of points is poised to bring about a revolution in acquiring and
generating three‐dimensional (3D) surface information of an object in 3D reconstruction …

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 an algorithm for fast global registration of partially overlap** 3D surfaces. The
algorithm operates on candidate matches that cover the surfaces. A single objective is …

In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and
descriptor for point cloud matching using weak supervision. Unlike many existing works, we …

We propose a novel end-to-end deep scene flow model, called PointPWC-Net, that directly
processes 3D point cloud scenes with large motions in a coarse-to-fine fashion. Flow …

We present an algorithm for tightly aligning two colored point clouds. The key idea is to
optimize a joint photometric and geometric objective that locks the alignment along both the …

Point cloud registration is the task of estimating the rigid transformation that aligns a pair of
point cloud fragments. We present an efficient and robust framework for pairwise registration …

We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans.
Given a set of 3D point correspondences, we build a deep neural network to address the …