Evaluating feature matching and ensemble strategies for monocular pose estimation in colonoscopy videos

Abstract

Treball fi de màster de: Erasmus Mundus joint Master in Artificial Intelligence (EMAI)

Supervisor: Professor Giorgio Grisetti Co-Supervisor: Dr Sophia Bano Academic Tutor: Professor Massimo Mecella

Colonoscopy, a key procedure for colorectal cancer screening, could benefit from 3D reconstruction and pose estimation for enhanced navigation, but robust feature matching remains an open challenge due to tissue deformation, variable illumination, and motion artefacts. This thesis evaluates three state-of-the-art learned feature matchers (DISK-LightGlue, GIM-LightGlue, and XFeat) and an ensemble approach for monocular pose recovery in synthetic colonoscopy videos. Results show that while the ensemble achieved the lowest rotational error (0.56°) and failure rate (0.5%) on registered sequences, trajectory recovery remained poor, and screening video evaluation was inconclusive due to pipeline limitations. These f indings suggest that current matchers alone are insufficient for reliable reconstruction in this domain, highlighting the need for deformation-aware models and more representative data before clinical application is feasible. Code is available at https://github.com/hduthie/thesis-colonoscopy-eval