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.
@article{arxiv.2203.04302,
title = {SuperPoint features in endoscopy},
author = {O. L. Barbed and F. Chadebecq and J. Morlana and J. M. Martínez-Montiel and A. C. Murillo},
journal= {arXiv preprint arXiv:2203.04302},
year = {2023}
}