Article

GPU-Accelerated Nearest Neighbor Search for 3D Registration

Authors:

Andreas NüchterAuthors Info & Claims

ICVS '09: Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems

Pages 194 - 203

https://doi.org/10.1007/978-3-642-04667-4_20

Published: 14 October 2009 Publication History

Abstract

Nearest Neighbor Search (NNS) is employed by many computer vision algorithms. The computational complexity is large and constitutes a challenge for real-time capability. The basic problem is in rapidly processing a huge amount of data, which is often addressed by means of highly sophisticated search methods and parallelism. We show that NNS based vision algorithms like the Iterative Closest Points algorithm (ICP) can achieve real-time capability while preserving compact size and moderate energy consumption as it is needed in robotics and many other domains. The approach exploits the concept of general purpose computation on graphics processing units (GPGPU) and is compared to parallel processing on CPU. We apply this approach to the 3D scan registration problem, for which a speed-up factor of 88 compared to a sequential CPU implementation is reported.

References

[1]

Arya, S., Mount, D.M.: Algorithms for Fast Vector Quantization. In: Proc. Data Compression Conference, pp. 381-390. IEEE Computer Society Press, Los Alamitos (1993)

[2]

Bustos, B., Deussen, O., Hiller, S., Keim, D.: A Graphics Hardware Accelerated Algorithm for Nearest Neighbor Search. In: Proc. of the 6th Int. Conf. on Computational Science, pp. 196-199 (May 2006)

[3]

Foley, T., Sugerman, J.: KD-Tree Acceleration Structures for a GPU Raytracer. In: Graphics Hardware, pp. 15-22 (July 2005)

[4]

Garcia, V., Debreuve, E., Barlaud, M.: Fast k Nearest Neighbor Search using GPU. In: Proc. Comp. Vision and Pattern Recognition Workshops (CVPRW), pp. 1-6 (June 2008)

[5]

Horn, D.R., Sugerman, J., Houston, M., Hanrahan, P.: Interactive k-D Tree GPU Raytracing. In: Proc. Symp. on Interactive 3D graphics and games, pp. 167-174 (April 2007)

[6]

Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239-256 (1992)

[7]

Jensen, H.W.: Realistic Image Synthesis Using Photon Mapping. AK Peters (July 2001)

[8]

Lieberman, M.D., Sankaranarayanan, J., Samet, H.: A Fast Similarity Join Algorithm Using Graphics Processing Units. In: Proc. of the 24th IEEE International Conference on Data Engineering, pp. 1111-1120 (May 2008)

[9]

Nüchter, A.: Parallelization of Scan Matching for Robotic 3D Mapping. In: Proceedings of the 3rd European Conference on Mobile Robots (September 2007)

[10]

Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6D SLAM with Approximate Data Association. In: Proc. of the 12th IEEE International Conference on Advanced Robotics (ICAR), pp. 242-249 (July 2005)

[11]

nVidia. NVIDIA CUDA Compute Unified Device Architecture Programming Guide. nVidia, version 2.0 edn. (June 2008)

[12]

Purcell, T.J., Donner, C., Cammarano, M., Jensen, H.W., Hanrahan, P.: Photon Mapping on Programmable Graphics Hardware. In: Doggett, M., Heidrich, W., Mark, W., Schillin, A. (eds.) Proc. of the ACM SIGGRAPH/EUROGRAPHICS Conf. on Graphics Hardware (2003)

[13]

Rozen, T., Boryczko, K., Alda, W.: GPU bucket sort algorithm with applications to nearest-neighbour search. In: Journal of the 16th Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision (February 2008)

[14]

Singh, S., Faloutsos, P.: SIMD Packet Techniques for Photon Mapping. In: Proc. of the IEEE/EG Symposium on Interactive Ray Tracing, pp. 87-94 (September 2007)

[15]

van Kooten, K., van den bergen, G., Telea, A.: GPU Gems 3, ch. 7, pp. 123-148. Addison Wesley Professional, Reading (2007)

[16]

Zhou, K., Hou, Q., Wang, R., Guo, B.: Real-Time KD-Tree Construction on Graphics Hardware. In: SIGGRAPH Asia 2008, p. 10 (April 2008)

Cited By

Mandarapu DNagarajan VPelenitsyn AKulkarni M(2024)Arkade: k-Nearest Neighbor Search With Non-Euclidean Distances using GPU Ray TracingProceedings of the 38th ACM International Conference on Supercomputing10.1145/3650200.3656601(14-25)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3650200.3656601
Qin PZhang CMa XShi Z(2022)High-Precision Motion Compensation for LiDAR Based on LiDAR OdometryWireless Communications & Mobile Computing10.1155/2022/58668682022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5866868
Lin YZhang ZTang HWang HHan S(2021)PointAcc: Efficient Point Cloud AcceleratorMICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3466752.3480084(449-461)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3466752.3480084
Show More Cited By

Index Terms

GPU-Accelerated Nearest Neighbor Search for 3D Registration
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

Index terms have been assigned to the content through auto-classification.

Recommendations

A GPGPU Algorithm for c-Approximate r-Nearest Neighbor Search in High Dimensions
IPDPSW '13: Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing Workshops and PhD Forum

Nearest Neighbor search is one of the simplest and most intuitive ideas in data mining. Due to it's simplicity and diverse utility, Nearest Neighbor search is often found to be the workhorse of a variety of data mining, machine learning, and computer ...
GPU-Accelerated Digital Halftoning by the Local Exhaustive Search
ISPDC '15: Proceedings of the 2015 14th International Symposium on Parallel and Distributed Computing

The main contribution of this paper is to show a new GPU implementation for the digital half toning by the local exhaustive search that can generate high quality binary images. We have considered programming issues of the GPU architecture to implement ...
Performance analysis of accelerated image registration using GPGPU
GPGPU-2: Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units

This paper presents a performance analysis of an accelerated 2-D rigid image registration implementation that employs the Compute Unified Device Architecture (CUDA) programming environment to take advantage of the parallel processing capabilities of ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

ICVS '09: Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems

October 2009

454 pages

ISBN:9783642046667

Editors:
Mario Fritz
ICSI & UC Berkeley, Berkeley, USA 94704
,
Bernt Schiele
Department of Computer Science, TU Darmstadt, Darmstadt, Germany 64289
,
Justus H. Piater
Department of Electrical Engineering and Computer Science, Montefiore Institute, University of Liège, Liège, Belgium 4000

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 October 2009

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mandarapu DNagarajan VPelenitsyn AKulkarni M(2024)Arkade: k-Nearest Neighbor Search With Non-Euclidean Distances using GPU Ray TracingProceedings of the 38th ACM International Conference on Supercomputing10.1145/3650200.3656601(14-25)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3650200.3656601
Qin PZhang CMa XShi Z(2022)High-Precision Motion Compensation for LiDAR Based on LiDAR OdometryWireless Communications & Mobile Computing10.1155/2022/58668682022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5866868
Lin YZhang ZTang HWang HHan S(2021)PointAcc: Efficient Point Cloud AcceleratorMICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3466752.3480084(449-461)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3466752.3480084
Xu TTian BZhu Y(2019)TigrisProceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3352460.3358259(629-642)Online publication date: 12-Oct-2019
https://dl.acm.org/doi/10.1145/3352460.3358259
Jung SSong SChang MPark S(2018)Range image registration based on 2D synthetic imagesComputer-Aided Design10.1016/j.cad.2017.08.00194:C(16-27)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.cad.2017.08.001
Wehr DRadkowski R(2018)Parallel kd-Tree Construction on the GPU with an Adaptive Split and Sort StrategyInternational Journal of Parallel Programming10.1007/s10766-018-0571-046:6(1139-1156)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10766-018-0571-0
Qiao WCréput J(2018)Spiral Search Method to GPU Parallel Euclidean Minimum Spanning Tree ProblemLearning and Intelligent Optimization10.1007/978-3-030-05348-2_2(16-30)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-030-05348-2_2
Hu LNooshabadi S(2017)Massive parallelization of approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matchingJournal of Visual Communication and Image Representation10.1016/j.jvcir.2017.01.01344:C(106-115)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.jvcir.2017.01.013
Xing LWang CHui K(2017)Coherent spherical range-search for dynamic points on GPUsComputer-Aided Design10.1016/j.cad.2017.01.00286:C(12-25)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1016/j.cad.2017.01.002
Tsai YSteinberger MPajźk DPulli K(2016)Fast ANN for High-Quality Collaborative FilteringComputer Graphics Forum10.1111/cgf.1271535:1(138-151)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1111/cgf.12715
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents