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Primitive fitting based on the efficient multiBaySAC algorithm.

Kang Z, Li Z - PLoS ONE (2015)

Bottom Line: Moreover, the updated version of the initial probability was implemented based on a memorable form of Bayes' Theorem, which describes the relationship between prior and posterior probabilities of a data point by determining whether the hypothesis set to which a data point belongs is correct.The proposed approach was tested using real and synthetic point clouds.Future work will aim at further optimizing this strategy through its application to other problems such as multiple point cloud co-registration and multiple image matching.

View Article: PubMed Central - PubMed

Affiliation: School of Land Science and Technology, China University of Geosciences, No. 29 Xueyuan Road, Haidian District, Beijing, 100083, China.

ABSTRACT
Although RANSAC is proven to be robust, the original RANSAC algorithm selects hypothesis sets at random, generating numerous iterations and high computational costs because many hypothesis sets are contaminated with outliers. This paper presents a conditional sampling method, multiBaySAC (Bayes SAmple Consensus), that fuses the BaySAC algorithm with candidate model parameters statistical testing for unorganized 3D point clouds to fit multiple primitives. This paper first presents a statistical testing algorithm for a candidate model parameter histogram to detect potential primitives. As the detected initial primitives were optimized using a parallel strategy rather than a sequential one, every data point in the multiBaySAC algorithm was assigned to multiple prior inlier probabilities for initial multiple primitives. Each prior inlier probability determined the probability that a point belongs to the corresponding primitive. We then implemented in parallel a conditional sampling method: BaySAC. With each iteration of the hypothesis testing process, hypothesis sets with the highest inlier probabilities were selected and verified for the existence of multiple primitives, revealing the fitting for multiple primitives. Moreover, the updated version of the initial probability was implemented based on a memorable form of Bayes' Theorem, which describes the relationship between prior and posterior probabilities of a data point by determining whether the hypothesis set to which a data point belongs is correct. The proposed approach was tested using real and synthetic point clouds. The results show that the proposed multiBaySAC algorithm can achieve a high computational efficiency (averaging 34% higher than the efficiency of the sequential RANSAC method) and fitting accuracy (exhibiting good performance in the intersection of two primitives), whereas the sequential RANSAC framework clearly suffers from over- and under-segmentation problems. Future work will aim at further optimizing this strategy through its application to other problems such as multiple point cloud co-registration and multiple image matching.

No MeSH data available.


Related in: MedlinePlus

multiBaySAC flowchart.A statistical testing algorithm for the candidate parameter set is performed to detect initial multiple primitives from the scattered point cloud so that every data point in the point cloud is assigned multiple prior inlier probabilities for the detected initial multiple primitives. With multiple prior inlier probabilities the fitting of multiple models based on the BaySAC process is then conducted simultaneously to estimate the optimized primitive models.
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pone.0117341.g001: multiBaySAC flowchart.A statistical testing algorithm for the candidate parameter set is performed to detect initial multiple primitives from the scattered point cloud so that every data point in the point cloud is assigned multiple prior inlier probabilities for the detected initial multiple primitives. With multiple prior inlier probabilities the fitting of multiple models based on the BaySAC process is then conducted simultaneously to estimate the optimized primitive models.

Mentions: Fig. 1 presents the multiBaySAC flowchart. First, a statistical testing algorithm for the candidate parameter set was applied to detect initial multiple primitives in the scattered point cloud. Every data point in the point cloud was then assigned to multiple prior inlier probabilities for detected initial multiple primitives. Each lists the probability that a point belongs to the corresponding primitive. Using multiple prior inlier probabilities, we then applied the fitting of multiple models using the BaySAC process in parallel to estimate the optimized primitive models.


Primitive fitting based on the efficient multiBaySAC algorithm.

Kang Z, Li Z - PLoS ONE (2015)

multiBaySAC flowchart.A statistical testing algorithm for the candidate parameter set is performed to detect initial multiple primitives from the scattered point cloud so that every data point in the point cloud is assigned multiple prior inlier probabilities for the detected initial multiple primitives. With multiple prior inlier probabilities the fitting of multiple models based on the BaySAC process is then conducted simultaneously to estimate the optimized primitive models.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4363901&req=5

pone.0117341.g001: multiBaySAC flowchart.A statistical testing algorithm for the candidate parameter set is performed to detect initial multiple primitives from the scattered point cloud so that every data point in the point cloud is assigned multiple prior inlier probabilities for the detected initial multiple primitives. With multiple prior inlier probabilities the fitting of multiple models based on the BaySAC process is then conducted simultaneously to estimate the optimized primitive models.
Mentions: Fig. 1 presents the multiBaySAC flowchart. First, a statistical testing algorithm for the candidate parameter set was applied to detect initial multiple primitives in the scattered point cloud. Every data point in the point cloud was then assigned to multiple prior inlier probabilities for detected initial multiple primitives. Each lists the probability that a point belongs to the corresponding primitive. Using multiple prior inlier probabilities, we then applied the fitting of multiple models using the BaySAC process in parallel to estimate the optimized primitive models.

Bottom Line: Moreover, the updated version of the initial probability was implemented based on a memorable form of Bayes' Theorem, which describes the relationship between prior and posterior probabilities of a data point by determining whether the hypothesis set to which a data point belongs is correct.The proposed approach was tested using real and synthetic point clouds.Future work will aim at further optimizing this strategy through its application to other problems such as multiple point cloud co-registration and multiple image matching.

View Article: PubMed Central - PubMed

Affiliation: School of Land Science and Technology, China University of Geosciences, No. 29 Xueyuan Road, Haidian District, Beijing, 100083, China.

ABSTRACT
Although RANSAC is proven to be robust, the original RANSAC algorithm selects hypothesis sets at random, generating numerous iterations and high computational costs because many hypothesis sets are contaminated with outliers. This paper presents a conditional sampling method, multiBaySAC (Bayes SAmple Consensus), that fuses the BaySAC algorithm with candidate model parameters statistical testing for unorganized 3D point clouds to fit multiple primitives. This paper first presents a statistical testing algorithm for a candidate model parameter histogram to detect potential primitives. As the detected initial primitives were optimized using a parallel strategy rather than a sequential one, every data point in the multiBaySAC algorithm was assigned to multiple prior inlier probabilities for initial multiple primitives. Each prior inlier probability determined the probability that a point belongs to the corresponding primitive. We then implemented in parallel a conditional sampling method: BaySAC. With each iteration of the hypothesis testing process, hypothesis sets with the highest inlier probabilities were selected and verified for the existence of multiple primitives, revealing the fitting for multiple primitives. Moreover, the updated version of the initial probability was implemented based on a memorable form of Bayes' Theorem, which describes the relationship between prior and posterior probabilities of a data point by determining whether the hypothesis set to which a data point belongs is correct. The proposed approach was tested using real and synthetic point clouds. The results show that the proposed multiBaySAC algorithm can achieve a high computational efficiency (averaging 34% higher than the efficiency of the sequential RANSAC method) and fitting accuracy (exhibiting good performance in the intersection of two primitives), whereas the sequential RANSAC framework clearly suffers from over- and under-segmentation problems. Future work will aim at further optimizing this strategy through its application to other problems such as multiple point cloud co-registration and multiple image matching.

No MeSH data available.


Related in: MedlinePlus