  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.
