Point clouds registration is a fundamental step of many point clouds processing pipelines; however, most algorithms are tested on data that are collected ad-hoc and not shared with the research community. These data often cover only a very limited set of use cases; therefore, the results cannot...
This package is a collection of GICP-based fast point cloud registration algorithms. It constains a multi-threaded GICP as well as multi-thread and GPU implementations of our voxelized GICP (VGICP) algorithm. All the implemented algorithms have the PCL registration interface so that they can be ...
Point cloud data registration is one of the key steps in 3D laser scanning data processing. At present, point cloud data registration has the problems of error and is too much time-consuming. In order to solve the above problems, a 3D point cloud data registration algorithm based on augmented...
2.1 Point cloud registration 2.2 Equivariant feature learning 3 METHOD 3.1 Preliminary 3.2 Descriptor construction 3.3 Rotation estimation 3.4 Modified RANSAC algorithms 3.5 Implementation details 4 EXPERIMENTS 4.1 Experimental protocol 4.2 Results on 3DMatch/3DLoMatch 4.3 Results on the ETH dataset 4.4 Abl...
Probreg is a library that implements point cloudregistration algorithms withprobablistic model. The point set registration algorithms using stochastic model are more robust than ICP(Iterative Closest Point). This package implements several algorithms using stochastic models and provides a simple interface wi...
Traditional ICP algorithms are prone to fall into the problem of local optimal solutions in 3D point cloud data registration. To this end, the ICP algorithm was further improved by optimizing the objective function and reducing the density of the point cloud, with the aim of improving the ...
Reconstructing three-dimensional (3D) point cloud model of maize plants can provide reliable data for its growth observation and agricultural machinery research. The existing data collection systems and registration methods have low collection efficiency
The point cloud data captured in an indoor environment contains points lying on the ground and ceiling planes, which confuses the point cloud registration algorithms. Some points are removed from the point cloud with these parameters: referenceVector - Normal to the ground plane. maxDistance - Maxim...
[arXiv]DPDist : Comparing Point Clouds Using Deep Point Cloud Distance, [paper] [arXiv]Single Shot 6D Object Pose Estimation, [paper] [arXiv]A Benchmark for Point Clouds Registration Algorithms, [paper] [code] [arXiv]TEASER: Fast and Certifiable Point Cloud Registration, [paper] [code] ...
4) Partial rigid registration Now let's back to the main text. First Section: An overview of point cloud registration Definition on point cloud registration: We first briefly give the definition of point cloud registration: It is to find a spatial transformation that moves the source shape towar...