SuperPoint features in endoscopy
This work addresses the problem of improving 3D modeling in endoscopy for medical applications, but it is incremental as it adapts an existing method to a specific domain.
The paper tackled the gap between research and medical practice by evaluating SuperPoint features on colonoscopy data, finding that adapted SuperPoint models achieved significantly higher matching quality than traditional methods like SIFT, with benefits such as avoiding specularity artifacts.
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.