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Iterative most-likely point registration (IMLP): a robust algorithm for computing optimal shape alignment.

Billings SD, Boctor EM, Taylor RH - PLoS ONE (2015)

Bottom Line: Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares.We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP.The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America.

ABSTRACT
We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP's probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.

No MeSH data available.


Related in: MedlinePlus

Registration errors for registering a source shape containing outliers to a mesh target under large misalignment. (Experiment 3B).Source shapes were randomly generated from the hip mesh (Fig. 1A), misaligned by [30, 60] mm / degrees, and registered back to the mesh. The test cases represent the different noise models used to generate noise on the source shape (Table 4). Outliers were added to the source shape constituting (A): 5%, (B): 10%, (C): 20%, and (D): 30% of the source points. For each test case, 300 randomized trials were conducted, with successful registrations being used to compute an average target registration error (TRE). The error bars provide approximate standard deviations of the reported average TRE values. The proposed IMLP algorithm was evaluated relative to standard ICP [1] and relative to a robust variant of ICP [4].
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pone.0117688.g004: Registration errors for registering a source shape containing outliers to a mesh target under large misalignment. (Experiment 3B).Source shapes were randomly generated from the hip mesh (Fig. 1A), misaligned by [30, 60] mm / degrees, and registered back to the mesh. The test cases represent the different noise models used to generate noise on the source shape (Table 4). Outliers were added to the source shape constituting (A): 5%, (B): 10%, (C): 20%, and (D): 30% of the source points. For each test case, 300 randomized trials were conducted, with successful registrations being used to compute an average target registration error (TRE). The error bars provide approximate standard deviations of the reported average TRE values. The proposed IMLP algorithm was evaluated relative to standard ICP [1] and relative to a robust variant of ICP [4].

Mentions: The registration accuracy for this study is presented in Figs. 3 and 4 for Experiments 3A and 3B, respectively. Figs. 3 and 4 are each divided into four sub-figures (A-D) corresponding to sub-experiments (i-iv) of Experiments 3A and 3B, respectively, for each level of outliers. The analysis to produce these results was conducted in the same manner as described for Experiment 2. As seen in the figures, IMLP widely outperforms ICP in terms of TRE for all test cases and performs marginally better overall than Robust ICP for 5% and 10% outliers and much better than Robust ICP at higher levels of outliers, where the TRE for Robust ICP approaches and even surpasses that of standard ICP. In contrast, IMLP’s TRE remains fairly stable up to 20% outliers and begins to increase at the 30% level.


Iterative most-likely point registration (IMLP): a robust algorithm for computing optimal shape alignment.

Billings SD, Boctor EM, Taylor RH - PLoS ONE (2015)

Registration errors for registering a source shape containing outliers to a mesh target under large misalignment. (Experiment 3B).Source shapes were randomly generated from the hip mesh (Fig. 1A), misaligned by [30, 60] mm / degrees, and registered back to the mesh. The test cases represent the different noise models used to generate noise on the source shape (Table 4). Outliers were added to the source shape constituting (A): 5%, (B): 10%, (C): 20%, and (D): 30% of the source points. For each test case, 300 randomized trials were conducted, with successful registrations being used to compute an average target registration error (TRE). The error bars provide approximate standard deviations of the reported average TRE values. The proposed IMLP algorithm was evaluated relative to standard ICP [1] and relative to a robust variant of ICP [4].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4352012&req=5

pone.0117688.g004: Registration errors for registering a source shape containing outliers to a mesh target under large misalignment. (Experiment 3B).Source shapes were randomly generated from the hip mesh (Fig. 1A), misaligned by [30, 60] mm / degrees, and registered back to the mesh. The test cases represent the different noise models used to generate noise on the source shape (Table 4). Outliers were added to the source shape constituting (A): 5%, (B): 10%, (C): 20%, and (D): 30% of the source points. For each test case, 300 randomized trials were conducted, with successful registrations being used to compute an average target registration error (TRE). The error bars provide approximate standard deviations of the reported average TRE values. The proposed IMLP algorithm was evaluated relative to standard ICP [1] and relative to a robust variant of ICP [4].
Mentions: The registration accuracy for this study is presented in Figs. 3 and 4 for Experiments 3A and 3B, respectively. Figs. 3 and 4 are each divided into four sub-figures (A-D) corresponding to sub-experiments (i-iv) of Experiments 3A and 3B, respectively, for each level of outliers. The analysis to produce these results was conducted in the same manner as described for Experiment 2. As seen in the figures, IMLP widely outperforms ICP in terms of TRE for all test cases and performs marginally better overall than Robust ICP for 5% and 10% outliers and much better than Robust ICP at higher levels of outliers, where the TRE for Robust ICP approaches and even surpasses that of standard ICP. In contrast, IMLP’s TRE remains fairly stable up to 20% outliers and begins to increase at the 30% level.

Bottom Line: Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares.We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP.The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America.

ABSTRACT
We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP's probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.

No MeSH data available.


Related in: MedlinePlus