Point Cloud Registration

81 papers with code • 5 benchmarks • 6 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

Greatest papers with code

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.

Classification General Classification +5

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.

Object Detection Point Cloud Registration +1

3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

andyzeng/3dmatch-toolbox CVPR 2017

To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions.

3D Reconstruction Point Cloud Registration

Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures

neka-nat/probreg 6 Jul 2018

Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, SLAM, object/scene recognition, and augmented reality.

Autonomous Navigation Point Cloud Registration +1

Learning multiview 3D point cloud registration

chrischoy/FCGF CVPR 2020

We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.

Point Cloud Registration

Fully Convolutional Geometric Features

chrischoy/FCGF International Conference on Computer vision 2019

Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking.

3D Feature Matching 3D Point Cloud Matching +3

DVI: Depth Guided Video Inpainting for Autonomous Driving

ApolloScapeAuto/dataset-api ECCV 2020

To get clear street-view and photo-realistic simulation in autonomous driving, we present an automatic video inpainting algorithm that can remove traffic agents from videos and synthesize missing regions with the guidance of depth/point cloud.

Autonomous Driving Image Inpainting +2

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.

Point Cloud Registration

The Perfect Match: 3D Point Cloud Matching with Smoothed Densities

zgojcic/3DSmoothNet CVPR 2019

Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.

3D Point Cloud Matching Point Cloud Registration

MaskNet: A Fully-Convolutional Network to Estimate Inlier Points

vinits5/learning3d 19 Oct 2020

We demonstrate these improvements on synthetic and real-world datasets.

Point Cloud Registration