kdtree | 易学教程

Indexing k-d tree?

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问题 I need to index a k-d tree, and be able to match part of it later. Is MySQL capable of ? Are there any alternatives? 回答1: You can try PostgreSQL 9.1.It can support Nearest Neighbor Search Indexes http://excoventures.com/talks/knn.pdf 回答2: I answer myself: current database can't index and search kd-tree. 来源： https://stackoverflow.com/questions/11015922/indexing-k-d-tree

Efficient way for SIFT descriptor matching

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问题 There are 2 images A and B. I extract the keypoints (a[i] and b[i]) from them. I wonder how can I determine the matching between a[i] and b[j], efficiently? The obvious method comes to me is to compare each point in A with each point in B. But it over time-consuming for large images databases. How can I just compare point a[i] with just b[k] where k is of small range? I heard that kd-tree may be a good choice, isn't it? Is there any good examples about kd-tree ? Any other suggestions? 回答1: KD

At what stage the training exactly takes place in FlannBasedMatcher in OpenCV?

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问题 The following code is in C++ and I am using OpenCV for my experiment. Suppose I am using kd-tree (FlannBasedMatcher) in the following way: //these are inputs to the code snippet below. //They are filled with suitable values Mat& queryDescriptors; vector<Training> &trainCollection; vector< vector<DMatch> >& matches; int knn; //setting flann parameters const Ptr<flann::IndexParams>& indexParams=new flann::KDTreeIndexParams(4); const Ptr<flann::SearchParams>& searchParams=new flann::SearchParams

Balancing KD-Tree: Which approach is more efficient?

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问题 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 elements in the vector and for each, compare them to the element I just found and put those smaller in newArray1, and those larger in newArray2 Recurse Way 2: Use quicksort O(nlogn) to sort all the elements in the array along a given axis, take the

Something faster than std::nth_element

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问题 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 alternative? Thanks in advance 回答1: Do you really need the nth element, or do you need an element "near" the middle? There are faster ways to get an element "near" the middle. One example goes roughly like: function rough_middle(container) divide container

kdtree for geospatial point search

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问题 I'm attempting to find nearest neighbors for point geometry with latitude and longitude information available to me. After much search I concluded that using a kd-tree based approach would be the best option. I have so far tried three different approaches with kd-tree, none of which have worked. Using mercator (UTM) projections. This was least useful as the distance calculation turned out to be completely wrong particularly since the points are spread all over the globe. Using latitude and

Simple C/C++ library for triangle-intersection acceleration structure

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问题 I'm raytracing and would like to speed it up via some acceleration structure (kd-tree, BVH, whatever). I don't want to code it up myself. What I've tried so far: Yanking the kd-tree out of pbrt. There are so many intra-dependencies that I couldn't succeed at this without pulling all of pbrt into my code. CGAL's AABB tree. Frustratingly, this seems to return only the point of intersection. Without knowing which triangle the point came from, I can't efficiently interpolate color over the

PCL kd-tree implementation extremely slow

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问题 I am using Point Cloud Library (PCL) based C++ implementation of kd-tree nearest neighbour(NN) searching. The data set contains about 2.2 million points. I am searching NN points for every other point. The search radius is set at 2.0. To fully compute that, it takes about 12 hours! I am using windows 64 bit machine with 4GB RAM. Is it very common for kd-tree searching? I wonder if there is any other c++ library for 3D point cloud data, which is faster. I heard about ANN C++ library & CGAL,

Balancing KD-Tree: Which approach is more efficient?

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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 elements in the vector and for each, compare them to the element I just found and put those smaller in newArray1, and those larger in newArray2 Recurse Way 2: Use quicksort O(nlogn) to sort all the elements in the array along a given axis, take the element at position arraysize/2 and store it in the current node. Then put all the elements from index 0

Something faster than std::nth_element

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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 alternative? Thanks in advance Yakk - Adam Nevraumont Do you really need the nth element, or do you need an element "near" the middle? There are faster ways to get an element "near" the middle. One example goes roughly like: function rough_middle(container) divide container into subsequences of length 5 find median of each subsequence of length 5 ~ O(k) * O(n