A k-d-tree (k-dimensional tree) is a data structure for storing points in multidimensional space. They can be used to efficiently query for whether a point exists, as well as to do Euclidean nearest-neighbor searches and searches inside of hyperdimensional rectangular regions.
Questions tagged [kdtree]
457 questions
6
votes
1 answer
python point indices in KDTree
Given a list of points, how can I get their indices in a KDTree?
from scipy import spatial
import numpy as np
#some data
x, y = np.mgrid[0:3, 0:3]
data = zip(x.ravel(), y.ravel())
points = [[0,1], [2,2]]
#KDTree
tree = spatial.cKDTree(data)
#…
a.smiet
- 1,727
- 3
- 21
- 36
6
votes
1 answer
KD-Tree in GLSL
after one day of trying to figure out how to implement a kd-tree in OpenGL/GLSL i am pretty frustrated ...
I declare my KD-Nodes like this in GLSL:
layout(std140) uniform node{
ivec4 splitPoint;
int dataPtr;
} nodes[1024];
SplitPoint holds the…
fho
- 6,787
- 26
- 71
6
votes
1 answer
Partition large amount of 3D point data
I need to partition a large set of 3D points (using C++). The points are stored on the HDD as binary float array, and the files are usually larger than 10GB.
I need to divide the set into smaller subsets that have a size less than 1GB.
The points…
blasalat
- 91
- 4
6
votes
2 answers
Something faster than std::nth_element
I'm working on a kd-tree implementation and I'm currently using std::nth_element for partition a vector of elements by their median. However std::nth_element takes 90% of the time of tree construction. Can anyone suggest a more efficient…
plasmacel
- 8,183
- 7
- 53
- 101
6
votes
1 answer
Nanoflann radius search
I have a doubt regarding the parameter search_radius in nanoflann's radiusSearch function. My code is this:
#include
#include
#include
BRabbit27
- 6,333
- 17
- 90
- 161
6
votes
1 answer
MemoryError in Python while using cKDTree().query_ball_tree
I have large 2D arrays with unsorted (X,Y) points, for which I need to know which points are in close proximity to each other (nearest-neighbor lookup). I have used cKDTree and query_ball_tree with succes for arrays with around 500,000 (X,Y) points.…
Eelco Verschelling
- 61
- 2
6
votes
3 answers
Balancing KD-Tree: Which approach is more efficient?
I'm trying to balance a set of (Million +) 3D points using a KD-tree and I have two ways of doing it.
Way 1:
Use an O(n) algorithm to find the arraysize/2-th largest element along a given axis and store it at the current node
Iterate over all the…
user1782677
- 1,963
- 5
- 26
- 48
6
votes
3 answers
Is kd-Tree an alternative to K-means clustering?
I'm working with BOW object detection and I'm working on the encoding stage. I have seen some implementations that use kd-Tree in the encoding stage, but most writings suggest that K-means clustering is the way to go.
What is the difference between…
mugetsu
- 4,228
- 9
- 50
- 79
6
votes
2 answers
QuadTree or Octree templatized implementation in C++
I'm going to write a templatized implementation of a KDTree, which for now should only work as Quadtree or Octree for a BarnesHut implementation.
The crucial point here is the design, I would like to specify the number of dimension where the tree is…
linello
- 8,451
- 18
- 63
- 109
5
votes
2 answers
Which spatial data structure (algorithm) is best for (searching in) a set of regions (spacial data)?
I have a set of regions (geo-fences) which are polygons. This set of data is fixed; so there is no need for insertion and deletion of data. Which data structure can be used for searching for regions that a query point (longitude, latitude) is in…
Kaveh Shahbazian
- 13,088
- 13
- 80
- 139
5
votes
3 answers
Sort coordinates of pointcloud by distance to previous point
Pointcloud of rope with desired start and end point
I have a pointcloud of a rope-like object with about 300 points. I'd like to sort the 3D coordinates of that pointcloud, so that one end of the rope has index 0 and the other end has index 300 like…
Xaagas
- 51
- 1
5
votes
0 answers
Why scipy 'cKDTree' is slower than 'cdist' for finding the nearest point?
I've been told throughout many references that KDTree is a fast way to find nearest neighbors for large data. My current issue is to find the nearest points in X for a given data A.
To elaborate, currently, X has 1,000,000 numerical data and A…
Cody Chung
- 629
- 1
- 6
- 15
5
votes
1 answer
Scipy: how to convert KD-Tree distance from query to kilometers (Python/Pandas)
This post builds upon this one.
I got a Pandas dataframe containing cities with their geo-coordinates (geodetic) as longitude and latitude.
import pandas as pd
df = pd.DataFrame([{'city':"Berlin", 'lat':52.5243700, 'lng':13.4105300},
…
Matthias
- 5,574
- 8
- 61
- 121
5
votes
2 answers
python sklearn KDTree with haversine distance
I try to create a KD tree of WGS84 coordinates and find neighbors within a certain radius
from sklearn.neighbors.dist_metrics import DistanceMetric
from sklearn.neighbors.kd_tree import KDTree
T = KDTree([[47.8665, 8.90123]],…
user307380
- 55
- 1
- 4
5
votes
1 answer
Ray Tracing F# - Missing triangles creates holes in figure: Hit properly?
I have worked on this quite a while and is stuck with this bug.
We have build a ray-tracer in F# for a school project. (Link explaining Ray tracer: https://blog.frogslayer.com/kd-trees-for-faster-ray-tracing-with-triangles/)
We have a made hit…
em0605
- 197
- 2
- 9