Point Cloud Registration

Point Cloud Registration is a fundamental problem in 3D computer vision and photogrammetry. Given several sets of points in different coordinate systems, the aim of registration is to find the transformation that best aligns all of them into a common coordinate system. Point Cloud Registration plays a significant role in many vision applications such as 3D model reconstruction, cultural heritage management, landslide monitoring and solar energy analysis. Source: Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Open3D: A Modern Library for 3D Data Processing

Content

TEASER: Fast and Certifiable Point Cloud Registration

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet

RPM-Net: Robust Point Matching Using Learned Features

PCRNet: Point Cloud Registration Network using PointNet Encoding

PREDATOR: Registration of 3D Point Clouds with Low Overlap

Deep Closest Point: Learning Representations for Point Cloud Registration

PRNet: Self-Supervised Learning for Partial-to-Partial Registration

Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences