Add to ORKGAbstract—The iterative closest point (ICP) algorithm is one of the most popular approaches to shape registration currently in use. At the core of ICP is the computationally-intensive determination of nearest neighbors (NN). As of now there has been no comprehensive analysis of competing search strategies for NN. This paper compares several libraries for nearest-neighbor search (NNS) on both simulated and real data with a focus on shape registration. In addition, we present a novel efficient implementation of NNS via k-d trees as well as a novel algorithm for NNS in octrees. Index Terms—shape registration, nearest neighbor search, k-d tree, octree, data structures
Nearest neighbor search is a basic primitive method used for machine learning and information retrie...
In this paper, we present a fast and versatile algorithm which can rapidly perform a variety of near...
In many computer vision problems, answering the nearest neighbor queries efficiently, especially in ...
There are many nearest neighbor algorithms tailormade for ICP, but most of them require special inpu...
There are many nearest neighbor algorithms tailormade for ICP, but most of them require special inpu...
Nowadays, the need to techniques, approaches, and algorithms to search on data is increased due to i...
The iterative closest point (ICP) algorithm is widely used for the registration of geometric data. O...
The thesis describes the basic concept of the K-nearest neighbors algorithm and its connection with ...
Abstract- In data mining, we have many nearest neighbor search algorithm.the nearest neighbor (NN) a...
The iterative closest point (ICP) algorithm is widely used for the registration of 3D geometric data...
9International audienceA common activity in many pattern recognition tasks, image processing or clus...
Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in tim...
[[abstract]]The problem of k-nearest neighbors (kNN) is to find the nearest k neighbors for a query ...
A simplified k nearest neighbour (knn) search for the R-tree family is proposed in this paper. This ...
Efficient search for nearest neighbors (NN) is a fundamental problem arising in a large variety of a...
Nearest neighbor search is a basic primitive method used for machine learning and information retrie...
In this paper, we present a fast and versatile algorithm which can rapidly perform a variety of near...
In many computer vision problems, answering the nearest neighbor queries efficiently, especially in ...
There are many nearest neighbor algorithms tailormade for ICP, but most of them require special inpu...
There are many nearest neighbor algorithms tailormade for ICP, but most of them require special inpu...
Nowadays, the need to techniques, approaches, and algorithms to search on data is increased due to i...
The iterative closest point (ICP) algorithm is widely used for the registration of geometric data. O...
The thesis describes the basic concept of the K-nearest neighbors algorithm and its connection with ...
Abstract- In data mining, we have many nearest neighbor search algorithm.the nearest neighbor (NN) a...
The iterative closest point (ICP) algorithm is widely used for the registration of 3D geometric data...
9International audienceA common activity in many pattern recognition tasks, image processing or clus...
Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in tim...
[[abstract]]The problem of k-nearest neighbors (kNN) is to find the nearest k neighbors for a query ...
A simplified k nearest neighbour (knn) search for the R-tree family is proposed in this paper. This ...
Efficient search for nearest neighbors (NN) is a fundamental problem arising in a large variety of a...
Nearest neighbor search is a basic primitive method used for machine learning and information retrie...
In this paper, we present a fast and versatile algorithm which can rapidly perform a variety of near...
In many computer vision problems, answering the nearest neighbor queries efficiently, especially in ...