PESAC, the Generalized Framework for RANSAC-Based Methods on SIMD Computing Platforms

This paper focuses on the computational optimization of RANSAC. We describe the Parallel Efficient Sample Consensus (PESAC) framework that allows efficient utilization of SIMD extensions and provides memory locality due to a special way of storing the input sequence of correspondences and generating...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.82151-82166
Main Authors: Rybakova, Ekaterina O., Trusov, Anton V., Limonova, Elena E., Skoryukina, Natalya S., Bulatov, Konstantin B., Nikolaev, Dmitry P.
Format: Article
Language:English
Subjects:
Accuracy
Classification
Computational modeling
Data models
Documents
Estimation
Failure rates
Graphics processing units
Homography estimation
identity document
Image matching
Iterative methods
localization
Mathematical models
Optimization
Pareto optimization
RANSAC
restrictors
Run time (computers)
SIMD
Task analysis
Tracking
USAC
Vector processing (computers)
vectorization
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Summary:This paper focuses on the computational optimization of RANSAC. We describe the Parallel Efficient Sample Consensus (PESAC) framework that allows efficient utilization of SIMD extensions and provides memory locality due to a special way of storing the input sequence of correspondences and generating a batch of samples per one main loop iteration. It is inspired by the USAC framework and has a block structure capable of implementing most modern RANSAC-based methods. We enhance it with individual blocks of sample and model restrictors that are aimed at the rejection of "bad" samples and model hypothesis before time-consuming model computation and verification blocks. We also provide a detailed description implementing 2D homography estimation problem in PESAC and benchmark the running time on the MIDV-2020 dataset of identity documents. Comparing to naive implementation, we accelerated our framework by 122 times for the document classification task (with a 6% increase in accuracy) and by 18 times for document tracking (with a 46% decrease in tracking failure rate) by using both restrictors and vector processing. This version also outperformed a number of USAC implementations from OpenCV-4.6.0 in runtime and accuracy of estimation (3 times faster, 6% greater accuracy for the classification task, and 2 times faster, 33% lower failure rate for tracking if comparing with USAC_MAGSAC).
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3301777