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A fast all nearest neighbor algorithm for applications involving large point-clouds

Jagan Sankaranarayanan, Hanan Samet, Amitabh Varshney
2007 Computers & graphics  
A fast kNN algorithm is presented that makes use of the locality of successive points whose k nearest neighbors are sought to reduce significantly the time needed to compute the neighborhood needed for  ...  All of these primitive operations require the seemingly repetitive process of finding the k nearest neighbors (kNNs) of each point. These algorithms are primarily designed to run in main memory.  ...  Special thanks are due to Chenyi Xia for providing us with the GORDER source code. The point models used in the paper are the creations of the following individuals or institutions.  ... 
doi:10.1016/j.cag.2006.11.011 fatcat:ijuhv3jwsfdbvdixbbqinmquty

Projective clustering and its application to surface reconstruction

Amit Mhatre, Piyush Kumar
2006 Proceedings of the twenty-second annual symposium on Computational geometry - SCG '06  
We use projective clustering to design and implement a fast surface reconstruction algorithm for point clouds that also works well for sharp edges and corners.  ...  Our method relies on two new approximation algorithms developed and implemented for the first time, namely, fast projective clustering and parallel dynamic nearest neighbor searching based on shifted quad-trees  ...  For every point, we compute the set of K nearest neighbors. For determining nearest neighbors, a dynamic approximate nearest neighbor search data structure based on Chan's algorithm [1] is used.  ... 
doi:10.1145/1137856.1137927 dblp:conf/compgeom/MhatreK06 fatcat:en3outfsyrcv5kwyihc3p7q2lq

Detection of Closed Sharp Feature Lines in Point Clouds for Reverse Engineering Applications [chapter]

Kris Demarsin, Denis Vanderstraeten, Tim Volodine, Dirk Roose
2006 Lecture Notes in Computer Science  
The algorithm is fast and gives good results for real-world point sets from industrial applications.  ...  The algorithm is fast and gives good results for real-world point sets from industrial applications. * kris.demarsin@cs.kuleuven.be † Metris N.V.,  ...  We can now understand why it was useful to keep the edges involving a large segment in the graph G all : the large neighboring segments are easily found.  ... 
doi:10.1007/11802914_42 fatcat:7xvmvndpajhhzmhzs6uzzr2qzm

A Fast k-Neighborhood Algorithm for Large Point-Clouds [article]

Jagan Sankaranarayanan, Hanan Samet, Amitabh Varshney
2006 Symposium Point-Based Graphics : [Proceedings]  
A fast k nearest neighbor algorithm is presented that makes use of the locality of successive points whose k nearest neighbors are sought to significantly reduce the time needed to compute the neighborhood  ...  This calls for more efficient methods of computing the k nearest neighbors of a large collection of points many of which are already in close proximity.  ...  Acknowledgments The authors would like to thank the anonymous reviewers for their useful comments and suggestions which helped improve the quality of this paper immensely.  ... 
doi:10.2312/spbg/spbg06/075-084 fatcat:3pgh77ptxbgotbsdy2ky4fbxv4

Detection of closed sharp edges in point clouds using normal estimation and graph theory

Kris Demarsin, Denis Vanderstraeten, Tim Volodine, Dirk Roose
2007 Computer-Aided Design  
The algorithm is fast and gives good results for real-world point sets from industrial applications.  ...  The reconstruction of a surface model from a point cloud is an important task in the reverse engineering of industrial parts.  ...  Acknowledgement The two mobile phone point clouds and the brick cloud are courtesy of Metris N.V. Belgium.  ... 
doi:10.1016/j.cad.2006.12.005 fatcat:pmi67z6tnfbyfbrmx5zytmd5ii

Efficient Similarity Indexing and Searching in High Dimensions [article]

Yu Zhong
2015 arXiv   pre-print
This paper presents a new approach for fast and effective searching and indexing of high dimensional features using random partitions of the feature space.  ...  We also compare its performance to that of the state-of-the-art locality sensitive hashing algorithm.  ...  Acknowledgement We would like to thank Alex Andoni of MIT for kindly providing the LSH implementation and answering questions about the LSH algorithm.  ... 
arXiv:1505.03090v1 fatcat:npl3qabpk5bilipzye3bqzvlpm

Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information

Zhen Zheng, Bingting Zha, Yu Zhou, Jinbo Huang, Youshi Xuchen, He Zhang
2022 Remote Sensing  
This paper proposes a single-stage adaptive multi-scale noise filtering algorithm for point clouds, based on feature information, which aims to mitigate the fact that the current laser point cloud noise  ...  The developed algorithm provides research thought for filtering pre-processing methods applicable in 3D measurements, remote sensing, and target recognition based on point clouds.  ...  Point Cloud Scatter Coefficient and Similarity Degree The mean distance of the k-nearest neighbors containing large-scale noise is greater than the mean distance of k-nearest neighbors not containing large-scale  ... 
doi:10.3390/rs14020367 fatcat:eshwcks5lfckto3y6tlf72o4iy

Are you using the right approximate nearest neighbor algorithm?

Stephen O'Hara, Bruce A. Draper
2013 2013 IEEE Workshop on Applications of Computer Vision (WACV)  
Many computer vision tasks such as large-scale image retrieval and nearest-neighbor classification perform similarity searches using Approximate Nearest Neighbor (ANN) indexes.  ...  These applications rely on the quality of ANN retrieval for success. Popular indexing methods for ANN queries include forests of kd-trees (KDT) and hierarchical k-means (HKM).  ...  Introduction Efficient and accurate methods for Approximate Nearest Neighbor (ANN) queries are important for large-scale computer vision applications such as similarity search and nearest-neighbor classification  ... 
doi:10.1109/wacv.2013.6474993 dblp:conf/wacv/OHaraD13 fatcat:k7vxtbsksjh3das5f2hbdrl364

Fast approximation for geometric classification of LiDAR returns

Xiaozhe Shi, Avideh Zakhor
2011 2011 18th IEEE International Conference on Image Processing  
In this paper we use gridded approximate nearest neighbor searches for fast classification of geometric features in large LiDAR point clouds.  ...  We show a factor of 10-20 speed up for both actual and simulated point clouds with little or no loss in classification performance.  ...  Finally, after the potential nearest neighbor points list is complete, the k nearest neighbors are returned by computing the k th farthest point from the reference point, and returning all points that  ... 
doi:10.1109/icip.2011.6116272 dblp:conf/icip/ShiZ11 fatcat:lsk7ku6b5jdzlkgvsb2qr5rjji

A Fast Point Clouds Registration Algorithm for Laser Scanners

Guangxuan Xu, Yajun Pang, Zhenxu Bai, Yulei Wang, Zhiwei Lu
2021 Applied Sciences  
However, the existing methods often suffer from low accuracy and low speed when registering large point clouds.  ...  Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods.  ...  Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11083426 fatcat:45toggttdffh3huzsmujscismm

FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

Timo Hackel, Jan D. Wegner, Konrad Schindler
2016 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
We describe an effective and efficient method for point-wise semantic classification of 3D point clouds.  ...  to process point clouds with many millions of points in a matter of minutes.  ...  METHOD Our goal is efficient point cloud labelling in terms of both runtime and memory, such that the algorithm is applicable to point clouds of realistic size.  ... 
doi:10.5194/isprs-annals-iii-3-177-2016 fatcat:rzd5fgq3uzacpje2wpmuo7vpzu

FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

Timo Hackel, Jan D. Wegner, Konrad Schindler
2016 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
We describe an effective and efficient method for point-wise semantic classification of 3D point clouds.  ...  to process point clouds with many millions of points in a matter of minutes.  ...  METHOD Our goal is efficient point cloud labelling in terms of both runtime and memory, such that the algorithm is applicable to point clouds of realistic size.  ... 
doi:10.5194/isprsannals-iii-3-177-2016 fatcat:xd5tolnc7jgmleuol2ytgdo76e

Fast relocalization for visual odometry using binary features

J. Straub, S. Hilsenbeck, G. Schroth, R. Huitl, A. Moller, E. Steinbach
2013 2013 IEEE International Conference on Image Processing  
By exploiting the properties of low-complexity binary feature descriptors, nearest-neighbor search is performed efficiently using Locality Sensitive Hashing.  ...  State-of-the-art visual odometry algorithms achieve remarkable efficiency and accuracy.  ...  This research project has been supported by the space agency of the German Aerospace Center with funds from the Federal Ministry of Economics and Technology on the basis of a resolution of the German Bundestag  ... 
doi:10.1109/icip.2013.6738525 dblp:conf/icip/StraubHSHMS13 fatcat:4unn5h4rznh2rjgbig27iincii

Fast geometric learning with symbolic matrices

Jean Feydy, Joan Alexis Glaunès, Benjamin Charlier, Michael M. Bronstein
2020 Neural Information Processing Systems  
nearest neighbor search -with the added benefit of flexibility.  ...  We perform an extensive evaluation on a broad class of problems: Gaussian modelling, K-nearest neighbors search, geometric deep learning, non-Euclidean embeddings and optimal transport theory.  ...  KeOps was first motivated by applications to computational anatomy, in collaboration with Alain Trouvé.  ... 
dblp:conf/nips/FeydyGCB20 fatcat:nlshkb3bt5hs7otmwf5gkgx6hm

OCTREE-BASED SIMD STRATEGY FOR ICP REGISTRATION AND ALIGNMENT OF 3D POINT CLOUDS

D. Eggert, S. Dalyot
2012 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
of large 3D point clouds, contributing to a qualitative and efficient application.  ...  The Iterative Closest Point algorithm for alignment of point clouds is one of the most commonly used algorithms for matching of rigid bodies.  ...  The result is a local 3D surface mesh of the given k nearest neighbor points.  ... 
doi:10.5194/isprsannals-i-3-105-2012 fatcat:f2cfeq43mzeirit4kqlkqp6nri
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