Add to ORKGThe k-nearest neighbour (kNN) problem appears in many different fields of computer science, such as computer animation and robotics. In crowd simulation, kNN queries are typically used by a collision-avoidance method to prevent unnecessary computations. Many different methods for finding these neighbours exist, but it is unclear which will work best in crowd simulations, an application which is characterised by low dimensionality and frequent change of the data points. We therefore compare several data structures for performing kNN queries. We find that the nanoflann implementation of a k-d tree offers the best performance by far on many different scenarios, processing 100,000 agents in about 35 milliseconds on a fast consumer PC
For many computer vision and machine learning problems, large training sets are key for good perform...
The k-Nearest Neighbour approach (k-NN) has been extensively used as a powerful non-parametric techn...
Simulation of human crowds can create plausible human trajectories, predict likely flows of pedestri...
The k-nearest neighbour (kNN) problem appears in many different fields of computer science, such as ...
International audienceIn this paper, we propose an efficient KNN service, called KPS (KNN-Peer-Sampl...
the k-nearest neighbors (kNN) algorithm is naturally used to search for the nearest neighbors of a t...
The K-Nearest Neighbors (KNN) algorithm is a simple but powerful technique used in the field of data...
The k-Nearest Neighbor method is one of the most popular techniques for both classification and regr...
For many computer vision and machine learning problems, large training sets are key for good perform...
Acceleration algorithms involving spatial partitioning methods are extensively used in crowd simulat...
K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering data. K-NN is e...
Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nea...
Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common ...
In general, algorithms to find continuous k-nearest neighbors has been researched on the location ba...
Nearest neighbor algorithms play many roles in our daily lives. From facial recognition to networkin...
For many computer vision and machine learning problems, large training sets are key for good perform...
The k-Nearest Neighbour approach (k-NN) has been extensively used as a powerful non-parametric techn...
Simulation of human crowds can create plausible human trajectories, predict likely flows of pedestri...
The k-nearest neighbour (kNN) problem appears in many different fields of computer science, such as ...
International audienceIn this paper, we propose an efficient KNN service, called KPS (KNN-Peer-Sampl...
the k-nearest neighbors (kNN) algorithm is naturally used to search for the nearest neighbors of a t...
The K-Nearest Neighbors (KNN) algorithm is a simple but powerful technique used in the field of data...
The k-Nearest Neighbor method is one of the most popular techniques for both classification and regr...
For many computer vision and machine learning problems, large training sets are key for good perform...
Acceleration algorithms involving spatial partitioning methods are extensively used in crowd simulat...
K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering data. K-NN is e...
Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nea...
Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common ...
In general, algorithms to find continuous k-nearest neighbors has been researched on the location ba...
Nearest neighbor algorithms play many roles in our daily lives. From facial recognition to networkin...
For many computer vision and machine learning problems, large training sets are key for good perform...
The k-Nearest Neighbour approach (k-NN) has been extensively used as a powerful non-parametric techn...
Simulation of human crowds can create plausible human trajectories, predict likely flows of pedestri...