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685,303
Divided kd Trees
, 1988
"... A variant of kd trees, the divided kd tree, is described that has some important advantages over ordinary kd trees. The divided kd tree is fully dynamic and allows for the insertion and deletion of points in O(log n) worstcase time. Moreover, divided kd trees allow for split and concatenate op ..."
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Cited by 6 (0 self)
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A variant of kd trees, the divided kd tree, is described that has some important advantages over ordinary kd trees. The divided kd tree is fully dynamic and allows for the insertion and deletion of points in O(log n) worstcase time. Moreover, divided kd trees allow for split and concatenate
Squarish kd trees
 SIAM Journal on Computing
, 2000
"... Abstract. We modify the kd tree on [0, 1] d by always cutting the longest edge instead of rotating through the coordinates. This modification makes the expected time behavior of lowerdimensional partial match queries behave as for perfectly balanced complete kd trees on n nodes. This is in contra ..."
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Cited by 8 (3 self)
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Abstract. We modify the kd tree on [0, 1] d by always cutting the longest edge instead of rotating through the coordinates. This modification makes the expected time behavior of lowerdimensional partial match queries behave as for perfectly balanced complete kd trees on n nodes
Distributed kd Trees
 In Proceedings 16th Conference of Chilean Computer Science Society (SCCC’96
, 1996
"... In this paper we present a generalization of the kd tree data structure suitable for an efficient management and querying in a distributed framework. We present optimal searching algorithm for exact, partial, and range search queries. Optimality is in the sense that (1) only servers that could have ..."
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Cited by 13 (5 self)
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In this paper we present a generalization of the kd tree data structure suitable for an efficient management and querying in a distributed framework. We present optimal searching algorithm for exact, partial, and range search queries. Optimality is in the sense that (1) only servers that could
Fast Ray Tracing Using KD Trees
, 1988
"... A hierarchical search structure for ray tracing based on kd trees is introduced. This data ..."
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Cited by 13 (0 self)
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A hierarchical search structure for ray tracing based on kd trees is introduced. This data
Approximate kd tree search for efficient ICP
 In Fourth International Conference on 3D Digital Imaging and Modeling (3DIM ’03
, 2003
"... A method is presented that uses an Approximate Nearest Neighbor method for determining correspondences within the Iterative Closest Point Algorithm. The method is based upon the kd tree. The standard kd tree uses a tentative backtracking search to identify nearest neighbors. In contrast, the Appro ..."
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Cited by 34 (0 self)
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A method is presented that uses an Approximate Nearest Neighbor method for determining correspondences within the Iterative Closest Point Algorithm. The method is based upon the kd tree. The standard kd tree uses a tentative backtracking search to identify nearest neighbors. In contrast
Parallel SAH kD Tree Construction
"... The kD tree is a wellstudied acceleration data structure for ray tracing. It is used to organize primitives in a scene to allow efficient execution of intersection operations between rays and the primitives. The highest quality kD tree can be obtained using greedy cost optimization based on a sur ..."
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The kD tree is a wellstudied acceleration data structure for ray tracing. It is used to organize primitives in a scene to allow efficient execution of intersection operations between rays and the primitives. The highest quality kD tree can be obtained using greedy cost optimization based on a
Cached kd tree search for ICP algorithms
"... The ICP (Iterative Closest Point) algorithm is the de facto standard for geometric alignment of threedimensional models when an initial relative pose estimate is available. The basis of ICP is the search for closest points. Since the development of ICP, kd trees have been used to accelerate the sea ..."
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Cited by 12 (1 self)
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The ICP (Iterative Closest Point) algorithm is the de facto standard for geometric alignment of threedimensional models when an initial relative pose estimate is available. The basis of ICP is the search for closest points. Since the development of ICP, kd trees have been used to accelerate
Optimizing Search Strategies in kd Trees
, 2001
"... Kd trees have been widely studied, yet their complete advantages are often not realized due to ineffective search implementations and degrading performance in high dimensional spaces. We outline an effective search algorithm for kd trees that combines an optimal depthfirst branch and bound (DFBB) ..."
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Cited by 4 (0 self)
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Kd trees have been widely studied, yet their complete advantages are often not realized due to ineffective search implementations and degrading performance in high dimensional spaces. We outline an effective search algorithm for kd trees that combines an optimal depthfirst branch and bound (DFBB
Speeding up Relief algorithms with kd trees
, 1998
"... There are certain problems in machine learning which desire special attention when we scale up the size of the data or move towards data mining. One of them is the problem of searching nearest neighbours of a given point in k dimensional space. If the space is ! k than kd trees can solve the prob ..."
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Cited by 5 (0 self)
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There are certain problems in machine learning which desire special attention when we scale up the size of the data or move towards data mining. One of them is the problem of searching nearest neighbours of a given point in k dimensional space. If the space is ! k than kd trees can solve
Analysis of Range Search for Random KD Trees
 Acta Informatica
, 1999
"... . We analyze the expected time complexity of range searching with kd trees in all dimensions when the data points are uniformly distributed in the unit hypercube. The partial match results of Flajolet and Puech are reproved using elementary probabilistic methods. In addition, we give asymptotic exp ..."
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Cited by 10 (2 self)
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. We analyze the expected time complexity of range searching with kd trees in all dimensions when the data points are uniformly distributed in the unit hypercube. The partial match results of Flajolet and Puech are reproved using elementary probabilistic methods. In addition, we give asymptotic
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