Approximate k-d tree search for efficient ICP - Greenspan, Yurick () Citation Context ...eighbour search in the centralloop is rather time consuming. One way to speed up the search is to use an efficient search
Methods to automatically recover the rigid transformations from matching sets of higher-level features were also presented. The advantage of these approaches is the reduction of the search space identified by two small sets of features, which results in efficient matching, but that should account for...
One of the most effective methods to perform ANN search is to use KD-Trees (K-Dimensional Trees). KD-Trees are a type of binary search tree that partitions data points into k-dimensional space, allowing for efficient querying of nearest neighbors. This blog post delves into the intricacies o...
KD-treeApproximate nearest neighbor searchParallel algorithmGPUCUDAImage descriptor matchingTo overcome the high computing cost associated with high-dimensional digital image descriptor matching, this paper presents a massively parallel approximate nearest neighbor search (ANNS) on K-dimensional tree (KD-tree...
To improve the runtime performance of the P-ANNS, we propose an efficient sliding window for a parallel buffered P-ANNS on KD-tree to mitigate the high cost of global memory accesses. When applied to high dimensional real-world image descriptor datasets, the proposed KD-tree construction and ...