Sparse Iterative Closest Point
Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point co...
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| Published in | Computer graphics forum Vol. 32; no. 5; pp. 113 - 123 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford, UK
Blackwell Publishing Ltd
01.08.2013
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point computations to establish correspondences between two data sets, and solving for the optimal transformation that brings these correspondences into alignment. A major difficulty for this approach is the sensitivity to outliers and missing data often observed in 3D scans. Most practical implementations of the ICP algorithm address this issue with a number of heuristics to prune or reweight correspondences. However, these heuristics can be unreliable and difficult to tune, which often requires substantial manual assistance. We propose a new formulation of the ICP algorithm that avoids these difficulties by formulating the registration optimization using sparsity inducing norms. Our new algorithm retains the simple structure of the ICP algorithm, while achieving superior registration results when dealing with outliers and incomplete data. The complete source code of our implementation is provided at http://lgg.epfl.ch/sparseicp. |
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| AbstractList | Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point computations to establish correspondences between two data sets, and solving for the optimal transformation that brings these correspondences into alignment. A major difficulty for this approach is the sensitivity to outliers and missing data often observed in 3D scans. Most practical implementations of the ICP algorithm address this issue with a number of heuristics to prune or reweight correspondences. However, these heuristics can be unreliable and difficult to tune, which often requires substantial manual assistance. We propose a new formulation of the ICP algorithm that avoids these difficulties by formulating the registration optimization using sparsity inducing norms. Our new algorithm retains the simple structure of the ICP algorithm, while achieving superior registration results when dealing with outliers and incomplete data. The complete source code of our implementation is provided at http://lgg.epfl.ch/sparseicp. Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point computations to establish correspondences between two data sets, and solving for the optimal transformation that brings these correspondences into alignment. A major difficulty for this approach is the sensitivity to outliers and missing data often observed in 3D scans. Most practical implementations of the ICP algorithm address this issue with a number of heuristics to prune or reweight correspondences. However, these heuristics can be unreliable and difficult to tune, which often requires substantial manual assistance. We propose a new formulation of the ICP algorithm that avoids these difficulties by formulating the registration optimization using sparsity inducing norms. Our new algorithm retains the simple structure of the ICP algorithm, while achieving superior registration results when dealing with outliers and incomplete data. The complete source code of our implementation is provided at http://lgg.epfl.ch/sparseicp . Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point computations to establish correspondences between two data sets, and solving for the optimal transformation that brings these correspondences into alignment. A major difficulty for this approach is the sensitivity to outliers and missing data often observed in 3D scans. Most practical implementations of the ICP algorithm address this issue with a number of heuristics to prune or reweight correspondences. However, these heuristics can be unreliable and difficult to tune, which often requires substantial manual assistance. We propose a new formulation of the ICP algorithm that avoids these difficulties by formulating the registration optimization using sparsity inducing norms. Our new algorithm retains the simple structure of the ICP algorithm, while achieving superior registration results when dealing with outliers and incomplete data. The complete source code of our implementation is provided at http://lgg.epfl.ch/sparseicp. [PUBLICATION ABSTRACT] |
| Author | Pauly, Mark Tagliasacchi, Andrea Bouaziz, Sofien |
| Author_xml | – sequence: 1 givenname: Sofien surname: Bouaziz fullname: Bouaziz, Sofien organization: École Polytechnique Fédérale de Lausanne (EPFL), Switzerland – sequence: 2 givenname: Andrea surname: Tagliasacchi fullname: Tagliasacchi, Andrea organization: École Polytechnique Fédérale de Lausanne (EPFL), Switzerland – sequence: 3 givenname: Mark surname: Pauly fullname: Pauly, Mark organization: École Polytechnique Fédérale de Lausanne (EPFL), Switzerland |
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| References_xml | – reference: Boyd S., Vandenberghe L.: Convex optimization. Cambridge University Press, 2004. 11. – reference: Candès E. J., Wakin M. B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25, 2 (2008). 2. – reference: Eldar Y. C., Kuppinger P., Bolcskei H.: Block-sparse signals: Uncertainty relations and efficient recovery. IEEE Trans. Signal Process. 58, 6 (2010), 3042-3054. 5. – reference: Jian B., Vemuri B.: Robust point set registration using gaussian mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 8 (2011), 1633-1645. 3. – reference: Aiger D., Mitra N. J., Cohen-Or D.: 4-points congruent sets for robust pairwise surface registration. ACM Trans. Graph. 27, 3 (2008), 85:1-85:10. 2, 3. – reference: Boyd S., Parikh N., Chu E., Peleato B., Eckstein J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning 3, 1 (2011), 1-122. 11. – reference: Friedman J. H., Bentley J. L., Finkel R. A.: An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software (TOMS) 3, 3 (1977), 209-226. 3, 4. – reference: Chartrand R.: Exact reconstruction of sparse signals via nonconvex minimization. IEEE Signal Processing Letters 14, 10 (2007), 707-710. 4, 5. – reference: Chetverikov D., Stepanov D., Krsek P.: Robust euclidean alignment of 3d point sets: the trimmed iterative closest point algorithm. Image and Vision Computing 23 (2005), 299-309. 8, 9. – reference: Weise T., Bouaziz S., Li H., Pauly M.: Realtime performance-based facial animation. ACM Trans. Graph. (Proc. SIGGRAPH) 30, 4 (2011), 77. 2. – reference: Fitzgibbon A. W.: Robust registration of 2d and 3d point sets. Image Vision Comput. 21 (2003), 1145-1153. 3. – reference: Marjanovic G., Solo V.: On ℓq optimization and matrix completion. IEEE Trans. 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| Snippet | Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most... |
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| SubjectTerms | Algorithms and systems Computer graphics Computer science Heuristic I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Geometric algorithms I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems Iterative methods languages Optimization Registration Robots Studies Three dimensional Three dimensional models |
| Title | Sparse Iterative Closest Point |
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