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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 of shapes having partial overlap. (Experiment 7).(A): The statue Laurana sub-divided into (B): front and (C): right half-sections, such that (D): a 50% overlap exists between the two sub-shapes. The sub-shapes were (E): misaligned by 10 mm and 10 degrees in a random direction and then registered using (F): CPD [20], (G): GICP [11], and (H): the proposed IMLP algorithm. Sub-figures (E-H) show the initial misalignment and the final registered alignments of the two shapes for the 6th randomized trial of Experiment 7, which involved 10 randomized trials in total.
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pone.0117688.g009: Registration of shapes having partial overlap. (Experiment 7).(A): The statue Laurana sub-divided into (B): front and (C): right half-sections, such that (D): a 50% overlap exists between the two sub-shapes. The sub-shapes were (E): misaligned by 10 mm and 10 degrees in a random direction and then registered using (F): CPD [20], (G): GICP [11], and (H): the proposed IMLP algorithm. Sub-figures (E-H) show the initial misalignment and the final registered alignments of the two shapes for the 6th randomized trial of Experiment 7, which involved 10 randomized trials in total.

Mentions: This study investigates a yet more challenging problem of registering two shapes that have only partial overlap, meaning there are regions of both the source and target shape that do not have any true correspondence with the other shape. To investigate this scenario, we use a model of the statue Laurana (Fig. 9A) provided by the Institute of Science and Technologies (ISTI-CNR), Pisa, Italy, which was downloaded under a Creative Commons License from http://vcg.isti.cnr.it/downloads/3dgallery/form_laurana.htm. A decimation was applied to the original mesh using the Quadric Edge Collapse Decimation in MeshLab [42] to reduce the model to 50,000 triangles, and the coordinate system was adjusted to position the origin at the mesh centroid. Two divisions of the mesh were then performed to extract the front (Fig. 9B) and right (Fig. 9C) half-sections of the model. A dense point cloud for the source shape was formed from the vertices of the right half-section, and a dense point cloud for the target shape was defined from the center points of the triangles of the front half-section. Thus, the region of overlap between the source and target shapes comprised 50% of each shape (Fig. 9D).


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

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

Registration of shapes having partial overlap. (Experiment 7).(A): The statue Laurana sub-divided into (B): front and (C): right half-sections, such that (D): a 50% overlap exists between the two sub-shapes. The sub-shapes were (E): misaligned by 10 mm and 10 degrees in a random direction and then registered using (F): CPD [20], (G): GICP [11], and (H): the proposed IMLP algorithm. Sub-figures (E-H) show the initial misalignment and the final registered alignments of the two shapes for the 6th randomized trial of Experiment 7, which involved 10 randomized trials in total.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0117688.g009: Registration of shapes having partial overlap. (Experiment 7).(A): The statue Laurana sub-divided into (B): front and (C): right half-sections, such that (D): a 50% overlap exists between the two sub-shapes. The sub-shapes were (E): misaligned by 10 mm and 10 degrees in a random direction and then registered using (F): CPD [20], (G): GICP [11], and (H): the proposed IMLP algorithm. Sub-figures (E-H) show the initial misalignment and the final registered alignments of the two shapes for the 6th randomized trial of Experiment 7, which involved 10 randomized trials in total.
Mentions: This study investigates a yet more challenging problem of registering two shapes that have only partial overlap, meaning there are regions of both the source and target shape that do not have any true correspondence with the other shape. To investigate this scenario, we use a model of the statue Laurana (Fig. 9A) provided by the Institute of Science and Technologies (ISTI-CNR), Pisa, Italy, which was downloaded under a Creative Commons License from http://vcg.isti.cnr.it/downloads/3dgallery/form_laurana.htm. A decimation was applied to the original mesh using the Quadric Edge Collapse Decimation in MeshLab [42] to reduce the model to 50,000 triangles, and the coordinate system was adjusted to position the origin at the mesh centroid. Two divisions of the mesh were then performed to extract the front (Fig. 9B) and right (Fig. 9C) half-sections of the model. A dense point cloud for the source shape was formed from the vertices of the right half-section, and a dense point cloud for the target shape was defined from the center points of the triangles of the front half-section. Thus, the region of overlap between the source and target shapes comprised 50% of each shape (Fig. 9D).

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