  Enabling the synthesis of arbitrarily novel viewpoint images within a
patient's stomach from pre-captured monocular gastroscopic images is a
promising topic in stomach diagnosis. Typical methods to achieve this objective
integrate traditional 3D reconstruction techniques, including
structure-from-motion (SfM) and Poisson surface reconstruction. These methods
produce explicit 3D representations, such as point clouds and meshes, thereby
enabling the rendering of the images from novel viewpoints. However, the
existence of low-texture and non-Lambertian regions within the stomach often
results in noisy and incomplete reconstructions of point clouds and meshes,
hindering the attainment of high-quality image rendering. In this paper, we
apply the emerging technique of neural radiance fields (NeRF) to monocular
gastroscopic data for synthesizing photo-realistic images for novel viewpoints.
To address the performance degradation due to view sparsity in local regions of
monocular gastroscopy, we incorporate geometry priors from a pre-reconstructed
point cloud into the training of NeRF, which introduces a novel geometry-based
loss to both pre-captured observed views and generated unobserved views.
Compared to other recent NeRF methods, our approach showcases high-fidelity
image renderings from novel viewpoints within the stomach both qualitatively
and quantitatively.
