  Robotic surgery has become a powerful tool for performing minimally invasive
procedures, providing advantages in dexterity, precision, and 3D vision, over
traditional surgery. One popular robotic system is the da Vinci surgical
platform, which allows preoperative information to be incorporated into live
procedures using Augmented Reality (AR). Scene depth estimation is a
prerequisite for AR, as accurate registration requires 3D correspondences
between preoperative and intraoperative organ models. In the past decade, there
has been much progress on depth estimation for surgical scenes, such as using
monocular or binocular laparoscopes [1,2]. More recently, advances in deep
learning have enabled depth estimation via Convolutional Neural Networks (CNNs)
[3], but training requires a large image dataset with ground truth depths.
Inspired by [4], we propose a deep learning framework for surgical scene depth
estimation using self-supervision for scalable data acquisition. Our framework
consists of an autoencoder for depth prediction, and a differentiable spatial
transformer for training the autoencoder on stereo image pairs without ground
truth depths. Validation was conducted on stereo videos collected in robotic
partial nephrectomy.
