Point Cloud Registration
184 papers with code • 22 benchmarks • 10 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.
Benchmarks
These leaderboards are used to track progress in Point Cloud Registration
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Libraries
Use these libraries to find Point Cloud Registration models and implementationsDatasets
KITTI
3DMatch
DeformingThings4D
3RScan
UrbanLoco
4DMatch
3DCSR dataset
FPv1
FAUST-partial
CODD
Most implemented papers
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
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.
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
Pacific-cyber/KUKA_Catch_Project
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Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios.
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
XuyangBai/PPF-FoldNet
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ECCV 2018
We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry.
The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
zgojcic/3DSmoothNet
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CVPR 2019
Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.
SampleNet: Differentiable Point Cloud Sampling
itailang/SampleNet
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CVPR 2020
As the size of the point cloud grows, so do the computational demands of these tasks.
Nonparametric Continuous Sensor Registration
The functions can be defined on arbitrary smooth manifolds where the action of a Lie group aligns them.
Learning multiview 3D point cloud registration
zgojcic/3D_multiview_reg
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CVPR 2020
We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
XuyangBai/D3Feat
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CVPR 2020
In this paper, we leverage a 3D fully convolutional network for 3D point clouds, and propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point.
Deep Global Registration
chrischoy/DeepGlobalRegistration
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CVPR 2020
We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans.
DVI: Depth Guided Video Inpainting for Autonomous Driving
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.
