Learning how to robustly estimate camera pose in endoscopic videos.

ABSTRACT:

Purpose

Surgical scene understanding plays a critical role in the technology stack of tomorrow's intervention-assisting systems in endoscopic surgeries. For this, tracking the endoscope pose is a key component, but remains challenging due to illumination conditions, deforming tissues and the breathing motion of organs.

Method

We propose a solution for stereo endoscopes that estimates depth and optical flow to minimize two geometric losses for camera pose estimation. Most importantly, we introduce two learned adaptive per-pixel weight mappings that balance contributions according to the input image content. To do so, we train a Deep Declarative Network to take advantage of the expressiveness of deep learning and the robustness of a novel geometric-based optimization approach.

SUBMITTER: Hayoz M 

PROVIDER: S-EPMC10329609 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature