About

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

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Datasets

Greatest papers with code

TEASER: Fast and Certifiable Point Cloud Registration

21 Jan 2020MIT-SPARK/TEASER-plusplus

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

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

CVPR 2017 andyzeng/3dmatch-toolbox

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

Learning multiview 3D point cloud registration

CVPR 2020 chrischoy/FCGF

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

POINT CLOUD REGISTRATION

Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures

6 Jul 2018neka-nat/probreg

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 SCENE RECOGNITION

PointNetLK: Robust & Efficient Point Cloud Registration using PointNet

CVPR 2019 hmgoforth/PointNetLK

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

CVPR 2019 zgojcic/3DSmoothNet

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

DVI: Depth Guided Video Inpainting for Autonomous Driving

ECCV 2020 ApolloScapeAuto/dataset-api

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 POINT CLOUD REGISTRATION VIDEO INPAINTING

Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration

12 Aug 2018YuePanEdward/GH-ICP

Then, we formulate the correspondence matching task as an energy function, which models the global similarity of keypoints on the hybrid spaces of BSC feature and Euclidean geometry.

POINT CLOUD REGISTRATION