E3CM: Epipolar-Constrained Cascade Correspondence Matching
Chenbo Zhou Shuai Su Qijun Chen Rui Fan
Accurate and robust correspondence matching is of utmost importance for
various 3D computer vision tasks. However, traditional explicit
programming-based methods often struggle to handle challenging scenarios, and
deep learning-based methods require large well-labeled datasets for network
training. In this article, we introduce Epipolar-Constrained Cascade
Correspondence (E3CM), a novel approach that addresses these limitations.
Unlike traditional methods, E3CM leverages pre-trained convolutional neural
networks to match correspondence, without requiring annotated data for any
network training or fine-tuning. Our method utilizes epipolar constraints to
guide the matching process and incorporates a cascade structure for progressive
refinement of matches. We extensively evaluate the performance of E3CM through
comprehensive experiments and demonstrate its superiority over existing
methods. To promote further research and facilitate reproducibility, we make
our source code publicly available at https://mias.group/E3CM.