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Purpose: Surgical scene understanding plays a critical role in the technologystack of tomorrow's intervention-assisting systems in endoscopic surgeries. Forthis, tracking the endoscope pose is a key component, but remains challengingdue to illumination conditions, deforming tissues and the breathing motion oforgans. Method: We propose a solution for stereo endoscopes that estimatesdepth and optical flow to minimize two geometric losses for camera poseestimation. Most importantly, we introduce two learned adaptive per-pixelweight mappings that balance contributions according to the input imagecontent. To do so, we train a Deep Declarative Network to take advantage of theexpressiveness of deep-learning and the robustness of a novel geometric-basedoptimization approach. We validate our approach on the publicly availableSCARED dataset and introduce a new in-vivo dataset, StereoMIS, which includes awider spectrum of typically observed surgical settings. Results: Our methodoutperforms state-of-the-art methods on average and more importantly, indifficult scenarios where tissue deformations and breathing motion are visible.We observed that our proposed weight mappings attenuate the contribution ofpixels on ambiguous regions of the images, such as deforming tissues.Conclusion: We demonstrate the effectiveness of our solution to robustlyestimate the camera pose in challenging endoscopic surgical scenes. Ourcontributions can be used to improve related tasks like simultaneouslocalization and mapping (SLAM) or 3D reconstruction, therefore advancingsurgical scene understanding in minimally-invasive surgery.

Learning How To Robustly Estimate Camera Pose in Endoscopic Videos