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
Latest papers with no code
PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer
Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics.
Learning Instance-Aware Correspondences for Robust Multi-Instance Point Cloud Registration in Cluttered Scenes
The superpoint correspondences are then extended to instance candidates at the fine level according to the instance masks.
Global Point Cloud Registration Network for Large Transformations
In this paper, we present ReLaTo (Registration for Large Transformations), an architecture that faces the cases where large transformations happen while maintaining good performance for local transformations.
Exploring Accurate 3D Phenotyping in Greenhouse through Neural Radiance Fields
This study investigates a learning-based phenotyping method using the Neural Radiance Field to achieve accurate in-situ phenotyping of pepper plants in greenhouse environments.
MEDPNet: Achieving High-Precision Adaptive Registration for Complex Die Castings
Due to their complex spatial structure and diverse geometric features, achieving high-precision and robust point cloud registration for complex Die Castings has been a significant challenge in the die-casting industry.
NeRF-Supervised Feature Point Detection and Description
Feature point detection and description is the backbone for various computer vision applications, such as Structure-from-Motion, visual SLAM, and visual place recognition.
PSS-BA: LiDAR Bundle Adjustment with Progressive Spatial Smoothing
The proposed method consists of a spatial smoothing module and a pose adjustment module, which combines the benefits of local consistency and global accuracy.
CLIPPER: Robust Data Association without an Initial Guess
When an informative initial estimation guess is available, the data association challenge is less acute; however, the existence of a high-quality initial guess is rare in most contexts.
PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations
We leverage a graph neural partial differential equation (PDE) based on Beltrami flow to obtain high-dimensional features and position embeddings for point clouds.
OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision
Without relying on learning or any labeled datasets, OptFlow achieves state-of-the-art performance for scene flow estimation on popular autonomous driving benchmarks.
