Space-Partitioning RANSAC

TL;DR

This paper introduces a space-partitioning technique to accelerate RANSAC model quality calculations by early rejection of inconsistent correspondences, achieving a 41% reduction in runtime without loss of accuracy.

Contribution

It presents a general, grid-based partitioning method that speeds up RANSAC by early correspondence rejection, applicable to various transformations and models.

Findings

Reduces RANSAC runtime by 41% on average.

Works with arbitrary transformations including fundamental matrix and homographies.

No deterioration in model accuracy observed.

Abstract

A new algorithm is proposed to accelerate RANSAC model quality calculations. The method is based on partitioning the joint correspondence space, e.g., 2D-2D point correspondences, into a pair of regular grids. The grid cells are mapped by minimal sample models, estimated within RANSAC, to reject correspondences that are inconsistent with the model parameters early. The proposed technique is general. It works with arbitrary transformations even if a point is mapped to a point set, e.g., as a fundamental matrix maps to epipolar lines. The method is tested on thousands of image pairs from publicly available datasets on fundamental and essential matrix, homography and radially distorted homography estimation. On average, it reduces the RANSAC run-time by 41% with provably no deterioration in the accuracy. It can be straightforwardly plugged into state-of-the-art RANSAC frameworks, e.g. VSAC.