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Cited by 1243D Shape Modeling and AnalysisAdvanced Image and Video Retrieval Techniques3D Surveying and Cultural Heritage

A multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images

Jost, T., Hugli, H.·2004·Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings.
DOI 10.1109/im.2003.1240278
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Abstract

The iterative closest point (ICP) algorithm is widely used for the registration of 3D geometric data. One of the main drawbacks of the algorithm is its quadratic time complexity O(N/sup 2/) with the number of points N. Consequently, several methods have been proposed to accelerate the process. We present a new solution for the speeding up of the ICP algorithm and special care is taken to avoid any tradeoff with the quality of the registration. The proposed solution combines a coarse to fine multiresolution approach with the neighbor search algorithm. The multiresolution approach permits to successively improve the registration using finer levels of representation and the neighbor search algorithm speeds up the closest point search by using a heuristic approach. Both multiresolution scheme and neighbor search algorithm main features are presented. Confirming the success of the proposed solution, typical results show that this combination permits to create a very fast ICP algorithm, with a closest point search complexity of O(N), while preserving the matching quality.

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Crossref · 10.1109/im.2003.1240278

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  • [62] further combines the above with a coarse-to-fine hierarchical search technique. Use of down-sampled data speeds up the first few iterations of ICP; the resolution is gradually increased to obtain more reliable alignment.

    “Hierarchical Approaches: [62] further combines the above with a coarse-to-fine hierarchical search technique.Use of down-sampled data speeds up the first few iterations of ICP; the resolution is gradually increased to obtain more reliable alignment.”

    From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [62]

  • [62] gives a fast ICP algorithm based on localization. They restrict the search for a new CPC at each iteration to the local neighborhood of the correspondence in the previous iteration, avoiding a global exhaustive search.

    “Localization: [62] gives a fast ICP algorithm (Section 4.1.1)based on localization.They restrict the search for a new CPC at each iteration to the local neighborhood of the correspondence in the previous iteration, avoiding a global exhaustive search.”

    From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [62]

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Authors
Jost, T., & Hugli, H.
Affiliations
University of Neuchâtel (CH)
Venue
Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings.
Publisher
IEEE
Published
2004-06-22
Pages
427-433
ISSN
1550-6185 (ISSN-L)
DOI
10.1109/im.2003.1240278
OpenAlex
W2148440329
Cited by
124
References
17
Citations by year
2024: 3 · 2022: 2 · 2021: 5 · 2020: 4 · 2019: 3 · 2018: 7 · 2017: 7 · 2016: 8 · 2015: 12 · 2014: 2 · 2013: 7 · 2012: 8
FWCI
18.51
Type
conference paper
Language
en
Open access
closed
Locations
  • International Conference on 3-D Digital Imaging and Modeling (3DIM) (publishedVersion)
  • reroDoc Digital Library (submittedVersion)
Topics
  • Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
  • Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
Keywords
iterative closest point, 3D registration, depth images, multi-resolution registration, neighbor search, computational complexity reduction, point cloud matching

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