Finding point correspondences between two views is generally based on the matching of local photometric descriptors. A subsequent geometric constraint ensures that the set of matching points is consistent with a realistic camera motion. Starting from a paper by Moisan and Stival, we propose an a contrario model for matching interest points based on descriptor similarity and geometric constraints. The resulting algorithm has adaptive matching thresholds and is able to detect point correspondences whose associated descriptors are not the first nearest neighbor. We also discuss the specific difficulties raised by images containing repeated patterns which are likely to introduce correspondences beyond the nearest neighbor.

Inverse-depth parameterization can successfully deal with the feature initialization problem in monocular simultaneous localization and mapping applications. However, it is redundant, and when multiple landmarks are initialized from the same image, it fails to enforce the ldquocommon originrdquo constraint. The authors propose two new variants that addresses both of these issues. The experimental results indicate that the proposed approach achieves a better performance at a lower computational cost.

Fundamental matrix estimation is difficult since it is often based on correspondences that are spoilt by noise and outliers. Outliers must be thrown out via robust statistics, and noise gives uncertainty. In this article we provide a closed-form formula for the uncertainty of the so-called 8 point algorithm, which is a basic tool for fundamental matrix estimation via robust methods. As an application, we modify a well established robust algorithm accordingly, leading to a new criterion to recover point correspondences under epipolar constraint, balanced by the uncertainty of the estimation. 1 Introduction and

This paper presents a probabilistic framework for computing correspondences and fundamental matrix in the structure from motion problem. Inspired by Moisan and Stival [1], we suggest using an a contrario model, which is a good answer to threshold problems in the robust filtering context. Contrary to most existing algorithms where perceptual correspondence setting and geometry evaluation are independent steps, the proposed algorithm is an all-in-one approach. We show that it is robust to repeated patterns which are usually difficult to unambiguously match and thus raise many problems in the fundamental matrix estimation.