Online Learning of Correspondences between Images

2026-08-13

AI Summary

This work proposes a novel method for establishing point correspondences across image sequences in real time under unknown 3D scene structure and imaging geometry. The approach introduces a channel-vector-based uncertainty density model and employs an online optimization mechanism driven by Neyman chi-square divergence to iteratively learn mappings between image point sets. By representing channel vectors with basis functions and integrating a density divergence criterion, the algorithm achieves rapid convergence and high-accuracy correspondence estimation under general imaging geometries. Experimental results demonstrate that the proposed method outperforms state-of-the-art techniques across multiple metrics, offering a compelling combination of real-time performance, robustness, and accuracy.

Abstract

We propose a novel method for iterative learning of point correspondences between image sequences. Points moving on surfaces in 3D space are projected into two images. Given a point in either view, the considered problem is to determine the corresponding location in the other view. The geometry and distortions of the projections are unknown as is the shape of the surface. Given several pairs of point-sets but no access to the 3D scene, correspondence mappings can be found by excessive global optimization or by the fundamental matrix if a perspective projective model is assumed. However, an iterative solution on sequences of point-set pairs with general imaging geometry is preferable. We derive such a method that optimizes the mapping based on Neyman's chi-square divergence between the densities representing the uncertainties of the estimated and the actual locations. The densities are represented as channel vectors computed with a basis function approach. The mapping between these vectors is updated with each new pair of images such that fast convergence and high accuracy are achieved. The resulting algorithm runs in real-time and is superior to state-of-the-art methods in terms of convergence and accuracy in a number of experiments.

Problem

• point correspondences
• online learning
• image sequences
• projection geometry
• 3D surfaces

Innovation

• online learning
• point correspondences
• Neyman's chi-square divergence
• channel representation
• iterative optimization