Single-view depth estimation refers to the ability to derive
three-dimensional information per pixel from a single two-dimensional image.
Single-view depth estimation is an ill-posed problem because there are multiple
depth solutions that explain 3D geometry from a single view. While deep neural
networks have been shown to be effective at capturing depth from a single view,
the majority of current methodologies are deterministic in nature. Accounting
for uncertainty in the predictions can avoid disastrous consequences when
applied to fields such as autonomous driving or medical robotics. We have
addressed this problem by quantifying the uncertainty of supervised single-view
depth for Bayesian deep neural networks. There are scenarios, especially in
medicine in the case of endoscopic images, where such annotated data is not
available. To alleviate the lack of data, we present a method that improves the
transition from synthetic to real domain methods. We introduce an
uncertainty-aware teacher-student architecture that is trained in a
self-supervised manner, taking into account the teacher uncertainty. Given the
vast amount of unannotated data and the challenges associated with capturing
annotated depth in medical minimally invasive procedures, we advocate a fully
self-supervised approach that only requires RGB images and the geometric and
photometric calibration of the endoscope. In endoscopic imaging, the camera and
light sources are co-located at a small distance from the target surfaces. This
setup indicates that brighter areas of the image are nearer to the camera,
while darker areas are further away. Building on this observation, we exploit
the fact that for any given albedo and surface orientation, pixel brightness is
inversely proportional to the square of the distance. We propose the use of
illumination as a strong single-view self-supervisory signal for deep neural
networks.

量化监督式单视图深度的不确定性的贝叶斯深度神经网络，通过光照作为单视图自我监督信号，提出了一种仅需 RGB 图像和内窥镜的几何和光度校准的完全自我监督方法。