Modern endoscopy relies on digital technology, from high-resolution imaging sensors and
displays to electronics connecting configurable illumination and actuation systems for …

Minimally invasive medicine has become mainstream because of its crucial clinical
significance in providing a low risk of postoperative complications, limited blood loss, short …

We present a self-supervised approach to training convolutional neural networks for dense
depth estimation from monocular endoscopy data without a priori modeling of anatomy or …

Contemporary endoscopic simultaneous localization and mapping (SLAM) methods
accurately compute endoscope poses; however, they only provide a sparse 3-D …

Real-time simultaneous localization and dense mapping is very helpful for providing virtual
reality and augmented reality for surgeons or even surgical robots. In this letter, we propose …

Monocular depth estimation is critical for endoscopists to perform spatial perception and 3D
navigation of surgical sites. However, most of the existing methods ignore the important …

In endoscopy, many applications (eg, surgical navigation) would benefit from a real-time
method that can simultaneously track the endoscope and reconstruct the dense 3D …

Background and objective: Gastrointestinal (GI) endoscopy represents a promising tool for
GI cancer screening. However, the limited field of view and uneven skills of endoscopists …

We propose a novel stereo laparoscopy video-based non-rigid SLAM method called EMDQ-
SLAM, which can incrementally reconstruct thee-dimensional (3D) models of soft tissue …

Conventional SLAM techniques strongly rely on scene rigidity to solve data association,
ignoring dynamic parts of the scene. In this work we present Semi-Direct DefSLAM (SD …