no code implementations • 26 Mar 2024 • Akshay Paruchuri, Samuel Ehrenstein, Shuxian Wang, Inbar Fried, Stephen M. Pizer, Marc Niethammer, Roni Sengupta
Monocular depth estimation in endoscopy videos can enable assistive and robotic surgery to obtain better coverage of the organ and detection of various health issues.
no code implementations • 13 Mar 2023 • Shuxian Wang, Yubo Zhang, Sarah K. McGill, Julian G. Rosenman, Jan-Michael Frahm, Soumyadip Sengupta, Stephen M. Pizer
Reconstructing a 3D surface from colonoscopy video is challenging due to illumination and reflectivity variation in the video frame that can cause defective shape predictions.
no code implementations • 19 Nov 2021 • Yubo Zhang, Jan-Michael Frahm, Samuel Ehrenstein, Sarah K. McGill, Julian G. Rosenman, Shuxian Wang, Stephen M. Pizer
Aiming to fundamentally improve the depth estimation quality for colonoscopy 3D reconstruction, in this work we have designed a set of training losses to deal with the special challenges of colonoscopy data.
no code implementations • 18 Mar 2021 • Yubo Zhang, Shuxian Wang, Ruibin Ma, Sarah K. McGill, Julian G. Rosenman, Stephen M. Pizer
In this work we focus on the lighting problem in colonoscopy videos.
1 code implementation • 15 Apr 2019 • Rui Wang, Stephen M. Pizer, Jan-Michael Frahm
Deep learning-based, single-view depth estimation methods have recently shown highly promising results.
no code implementations • 17 May 2018 • Rui Wang, Jan-Michael Frahm, Stephen M. Pizer
Our method produces superior results to the state-of-the-art learning-based, single- or two-view depth estimation methods on both indoor and outdoor benchmark datasets.