A Solution for Multi-Alignment by Transformation Synchronisation

TL;DR

This paper introduces a novel method for synchronizing multiple transformations in computer vision, effectively handling noisy, incomplete, or incorrect pairwise data to achieve consistent object alignment.

Contribution

It proposes a new approach that retrieves noise-free transformations from the null space of a matrix derived from pairwise alignments, improving robustness over iterative methods.

Findings

Effective with noisy data

Handles missing data well

Robust to wrong correspondences

Abstract

The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations between any pair of objects are free of noise. However, if only noisy relative transformations are available (e.g. due to missing data or wrong correspondences) the iterative methods may fail. Based on the observation that the underlying noise-free transformations can be retrieved from the null space of a matrix that can directly be obtained from pairwise alignments, this paper presents a novel method for the synchronisation of pairwise transformations such that they are transitively consistent. Simulations demonstrate that for noisy transformations, a large proportion of missing data and even for wrong correspondence assignments the method delivers encouraging results.