Point Cloud Registration
185 papers with code • 22 benchmarks • 11 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Point Cloud Registration models and implementationsDatasets
KITTI
3DMatch
DeformingThings4D
3RScan
UrbanLoco
4DMatch
3DCSR dataset
FPv1
FAUST-partial
VBR
Latest papers
Iterative Feedback Network for Unsupervised Point Cloud Registration
ivanxie416/ifnet
•
•
In this paper, we propose a novel Iterative Feedback Network (IFNet) for unsupervised point cloud registration, in which the representation of low-level features is efficiently enriched by rerouting subsequent high-level features.
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning
xmu-qcj/e2pnet
•
•
NeurIPS 2023
While registration of 2D RGB images to 3D point clouds is a long-standing problem in computer vision, no prior work studies 2D-3D registration for event cameras.
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration
Specifically, we first incorporate the projected images from the point clouds and fuse the cross-modal features using the attention mechanism.
SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation
jiang-hb/diffusionreg
•
•
NeurIPS 2023
By contrast, the SE(3) reverse process focuses on learning a denoising network that refines the noisy transformation step-by-step, bringing it closer to the optimal transformation for accurate pose estimation.
Colmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration
In contrast, mapping methods based on LiDAR scans are popular in large-scale urban scene reconstruction due to their precise distance measurements, a capability fundamentally absent in visual-based approaches.
Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras
This paper proposes an equivariant neural network that takes data in any semi-simple Lie algebra as input.
FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators
Matching cross-modality features between images and point clouds is a fundamental problem for image-to-point cloud registration.
EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization
minnjung/ep2p-loc
•
•
ICCV 2023
Visual localization is the task of estimating a 6-DoF camera pose of a query image within a provided 3D reference map.
DReg-NeRF: Deep Registration for Neural Radiance Fields
aibluefisher/dreg-nerf
•
•
ICCV 2023
Although Neural Radiance Fields (NeRF) is popular in the computer vision community recently, registering multiple NeRFs has yet to gain much attention.
PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration
Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications.
