  Dense depth estimation and 3D reconstruction of a surgical scene are crucial
steps in computer assisted surgery. Recent work has shown that depth estimation
from a stereo images pair could be solved with convolutional neural networks.
However, most recent depth estimation models were trained on datasets with
per-pixel ground truth. Such data is especially rare for laparoscopic imaging,
making it hard to apply supervised depth estimation to real surgical
applications. To overcome this limitation, we propose SADepth, a new
self-supervised depth estimation method based on Generative Adversarial
Networks. It consists of an encoder-decoder generator and a discriminator to
incorporate geometry constraints during training. Multi-scale outputs from the
generator help to solve the local minima caused by the photometric reprojection
loss, while the adversarial learning improves the framework generation quality.
Extensive experiments on two public datasets show that SADepth outperforms
recent state-of-the-art unsupervised methods by a large margin, and reduces the
gap between supervised and unsupervised depth estimation in laparoscopic
images.
