118 citations · 173 across the 8 of their papers we have counts for
8 papers
SAGE: SLAM with Appearance and Geometry Prior for Endoscopy
Xingtong Liu, Zhaoshuo Li, Masaru Ishii +3
In endoscopy, many applications (e.g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry…
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers
Zhaoshuo Li, Xingtong Liu, Nathan Drenkow +4
Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem…
Extremely Dense Point Correspondences using a Learned Feature Descriptor
Xingtong Liu, Yiping Zheng, Benjamin Killeen +4
High-quality 3D reconstructions from endoscopy video play an important role in many clinical applications, including surgical navigation where they enable direct video-CT registrat…
Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment
Xingtong Liu, Maia Stiber, Jindan Huang +4
Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understan…
2018 Robotic Scene Segmentation Challenge
Max Allan, Satoshi Kondo, Sebastian Bodenstedt +38
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward ki…
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video
Xingtong Liu, Ayushi Sinha, Masaru Ishii +3
We present a self-supervised learning-based pipeline for dense 3D reconstruction from full-length monocular endoscopic videos without a priori modeling of anatomy or shading. Our m…
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods
Xingtong Liu, Ayushi Sinha, Masaru Ishii +4
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 sha…
Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy
Xingtong Liu, Ayushi Sinha, Mathias Unberath +4
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 sha…