  Gastroendoscopy has been a clinical standard for diagnosing and treating
conditions that affect a part of a patient's digestive system, such as the
stomach. Despite the fact that gastroendoscopy has a lot of advantages for
patients, there exist some challenges for practitioners, such as the lack of 3D
perception, including the depth and the endoscope pose information. Such
challenges make navigating the endoscope and localizing any found lesion in a
digestive tract difficult. To tackle these problems, deep learning-based
approaches have been proposed to provide monocular gastroendoscopy with
additional yet important depth and pose information. In this paper, we propose
a novel supervised approach to train depth and pose estimation networks using
consecutive endoscopy images to assist the endoscope navigation in the stomach.
We firstly generate real depth and pose training data using our previously
proposed whole stomach 3D reconstruction pipeline to avoid poor generalization
ability between computer-generated (CG) models and real data for the stomach.
In addition, we propose a novel generalized photometric loss function to avoid
the complicated process of finding proper weights for balancing the depth and
the pose loss terms, which is required for existing direct depth and pose
supervision approaches. We then experimentally show that our proposed
generalized loss performs better than existing direct supervision losses.
