SuperPoint features in endoscopy

Authors:
  • OBO. L. Barbed
    O. L. Barbed
  • FCF. Chadebecq
    F. Chadebecq
  • JMJ. Morlana
    J. Morlana
  • JMJ. M. Martínez-Montiel
    J. M. Martínez-Montiel
  • AMA. C. Murillo
    A. C. Murillo
Preprint
Publisher website
Google Scholar
Abstract
There is often a significant gap between research results and applicability in routine medical practice. This work studies the performance of well-known local features on a medical dataset captured during routine colonoscopy procedures. Local feature extraction and matching is a key step for many computer vision applications, specially regarding 3D modelling. In the medical domain, handcrafted local features such as SIFT, with public pipelines such as COLMAP, are still a predominant tool for this kind of tasks. We explore the potential of the well known self-supervised approach SuperPoint, present an adapted variation for the endoscopic domain and propose a challenging evaluation framework. SuperPoint based models achieve significantly higher matching quality than commonly used local features in this domain. Our adapted model avoids features within specularity regions, a frequent and problematic artifact in endoscopic images, with consequent benefits for matching and reconstruction results.