Point Cloud Registration

122 papers with code • 11 benchmarks • 8 datasets

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


Use these libraries to find Point Cloud Registration models and implementations
3 papers

Most implemented papers

Open3D: A Modern Library for 3D Data Processing

isl-org/Open3D 30 Jan 2018

The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python.

PointNetLK: Robust & Efficient Point Cloud Registration using PointNet

hmgoforth/PointNetLK CVPR 2019

To date, the successful application of PointNet to point cloud registration has remained elusive.

PCRNet: Point Cloud Registration Network using PointNet Encoding

vinits5/learning3d 21 Aug 2019

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 and shape completion.

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

tensorflow/models CVPR 2020

We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.

TEASER: Fast and Certifiable Point Cloud Registration

MIT-SPARK/TEASER-plusplus 21 Jan 2020

We propose the first fast and certifiable algorithm for the registration of two sets of 3D points in the presence of large amounts of outlier correspondences.

RPM-Net: Robust Point Matching using Learned Features

yewzijian/RPMNet CVPR 2020

The hard assignments of closest point correspondences based on spatial distances are sensitive to the initial rigid transformation and noisy/outlier points, which often cause ICP to converge to wrong local minima.

Deep Closest Point: Learning Representations for Point Cloud Registration

WangYueFt/dcp ICCV 2019

To address local optima and other difficulties in the ICP pipeline, we propose a learning-based method, titled Deep Closest Point (DCP), inspired by recent techniques in computer vision and natural language processing.

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

WangYueFt/prnet NeurIPS 2019

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

PREDATOR: Registration of 3D Point Clouds with Low Overlap

ShengyuH/OverlapPredator CVPR 2021

We introduce PREDATOR, a model for pairwise point-cloud registration with deep attention to the overlap region.

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

TuSimple/LiDAR_SOT 10 Mar 2021

The code and protocols for our benchmark and algorithm are available at https://github. com/TuSimple/LiDAR_SOT/.