Point Cloud Registration
181 papers with code • 14 benchmarks • 9 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.
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Use these libraries to find Point Cloud Registration models and implementationsLatest papers
VRHCF: Cross-Source Point Cloud Registration via Voxel Representation and Hierarchical Correspondence Filtering
Our method exhibits versatile applicability and excels in both traditional homologous registration and challenging cross-source registration scenarios.
FastMAC: Stochastic Spectral Sampling of Correspondence Graph
As such, the core of our method is the stochastic spectral sampling of correspondence graph.
Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension
Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions.
PCR-99: A Practical Method for Point Cloud Registration with 99% Outliers
We propose a robust method for point cloud registration that can handle both unknown scales and extreme outlier ratios.
CLIPPER+: A Fast Maximal Clique Algorithm for Robust Global Registration
The registration problem can be formulated as a graph and solved by finding its maximum clique.
Registration of algebraic varieties using Riemannian optimization
Our approach is particularly useful when the two point clouds describe different parts of an objects (which may not even be overlapping), on the condition that the surface of the object may be well approximated by a set of polynomial equations.
Iterative Feedback Network for Unsupervised Point Cloud Registration
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
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
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.