A new point matching algorithm for non-rigid registration
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Schulman et al. propose to transfer a demonstration trajectory using non-rigid registration between the observed scene and a demonstrated scene, using Thin Plate Spline Robust Point Matching on point clouds.
“Schulman et al. (2016) [116], on the other hand, propose to transfer the demonstration trajectory using non-rigid registration between the observed scene and a demonstrated scene. They conduct the registration using the Thin Plate Spline Robust Point Matching (TPS-RPM) approach proposed by Chui and Rangarajan (2003) [117] on the scene represented as point clouds.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [117]
Although many non-rigid deformation models are different in formulation, they all have one common characteristic: they all try to preserve some kind of smoothness.
“Although many of these non-rigid deformation models are different in terms of formulation, they all have one common characteristic.Whether they use adjacency similarity, gradient or Laplacian fields [28] (first-order), thin-plate splines [35] (second-order), or various regularizers (Section 4.2.6), they all try to preserve some kind of smoothness.”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [35]
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“They conduct the registration using the Thin Plate Spline Robust Point Matching (TPS-RPM) approach proposed by Chui and Rangarajan (2003) [117] on the scene represented as point clouds.”
MethodLee, A. X., Goldstein, M. A., Barratt, S. T., & Abbeel, P. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139289 · Cited by 12 · see citing works
- Authors
- Chui, H., & Rangarajan, A.
- Affiliations
- University of Florida (US)
- Corresponding author
- Rangarajan, Anand
- ORCID
- Rangarajan, Anand
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2003-02
- Volume
- 89
- Issue
- 2-3
- Pages
- 114-141
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2171490473
- Cited by
- 1,560
- References
- 56
- Citations by year
- 2026: 24 · 2025: 20 · 2024: 34 · 2023: 36 · 2022: 50 · 2021: 52 · 2020: 66 · 2019: 79 · 2018: 75 · 2017: 85 · 2016: 111 · 2015: 121 · 2014: 112 · 2013: 86 · 2012: 86
- FWCI
- 42.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Vision and Image Understanding (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- 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
- Keywords
- non-rigid registration, point matching, feature-based registration, correspondence problem, outlier removal, thin-plate splines, cortical anatomy, brain mapping
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