Abstract: Methods, systems, and computer readable media for deriving a three-dimensional (3D) textured surface from endoscopic video are disclosed. According to one method for deriving a 3D textured surface from endoscopic video, the method comprises: performing video frame preprocessing to identify a plurality of video frames of an endoscopic video, wherein the video frame preprocessing includes informative frame selection, specularity removal, and key-frame selection; generating, using a neural network or a shape-from-motion-and-shading (SfMS) approach, a 3D textured surface from the plurality of video frames; and optionally registering the 3D textured surface to at least one CT image.

Abstract: Methods, systems, and computer readable media for deriving a three-dimensional (3D) textured surface from endoscopic video are disclosed. According to one method for deriving a 3D textured surface from endoscopic video, the method comprises: performing video frame preprocessing to identify a plurality of video frames of an endoscopic video, wherein the video frame preprocessing includes informative frame selection, specularity removal, and key-frame selection; generating, using a neural network or a shape-from-motion-and-shading (SfMS) approach, a 3D textured surface from the plurality of video frames; and optionally registering the 3D textured surface to at least one CT image.

Abstract: Methods, systems, and computer readable media for deriving a three-dimensional (3D) textured surface from endoscopic video are disclosed. According to one method for deriving a 3D textured surface from endoscopic video, the method comprises: performing video frame preprocessing to identify a plurality of video frames of an endoscopic video, wherein the video frame preprocessing includes informative frame selection, specularity removal, and key-frame selection; generating, using a neural network or a shape-from-motion-and-shading (SfMS) approach, a 3D textured surface from the plurality of video frames; and optionally registering the 3D textured surface to at least one CT image.

Abstract: Methods, systems, and computer-readable media for utilizing radial distortion to estimate a pose configuration are disclosed. According to one aspect, the method includes receiving, from each of a plurality of camera devices, an input pixel row of a radially distorted image and conducting a row comparison between each of the input pixel rows and a respectively associated synthesized pixel row. The method further includes approximating, for each row comparison, a span of a curve in an image space with a plurality of segments and computing, for each of the plurality of segments, a constraint. The method also includes utilizing the constraints to estimate a pose configuration.