BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus

TL;DR

This paper introduces BANSAC, a Bayesian network-based adaptive sampling method for RANSAC that improves efficiency and accuracy by dynamically updating inlier scores and optimizing stopping criteria.

Contribution

The paper proposes a novel dynamic Bayesian network for adaptive sampling in RANSAC, enhancing robustness and reducing computational cost without requiring prior inlier information.

Findings

Outperforms baseline methods in accuracy

Requires less computational time

Works with or without prior scoring

Abstract

RANSAC-based algorithms are the standard techniques for robust estimation in computer vision. These algorithms are iterative and computationally expensive; they alternate between random sampling of data, computing hypotheses, and running inlier counting. Many authors tried different approaches to improve efficiency. One of the major improvements is having a guided sampling, letting the RANSAC cycle stop sooner. This paper presents a new adaptive sampling process for RANSAC. Previous methods either assume no prior information about the inlier/outlier classification of data points or use some previously computed scores in the sampling. In this paper, we derive a dynamic Bayesian network that updates individual data points' inlier scores while iterating RANSAC. At each iteration, we apply weighted sampling using the updated scores. Our method works with or without prior data point scorings. In addition, we use the updated inlier/outlier scoring for deriving a new stopping criterion for the RANSAC loop. We test our method in multiple real-world datasets for several applications and obtain state-of-the-art results. Our method outperforms the baselines in accuracy while needing less computational time.