Search Results for author: Xingtong Liu

Found 13 papers, 8 papers with code

Neighborhood Normalization for Robust Geometric Feature Learning

1 code implementation CVPR 2021 Xingtong Liu, Benjamin D. Killeen, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath

Extracting geometric features from 3D models is a common first step in applications such as 3D registration, tracking, and scene flow estimation.

Scene Flow Estimation

Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers

1 code implementation5 Nov 2020 Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X. Creighton, Russell H. Taylor, Mathias Unberath

Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth.

Stereo Depth Estimation

Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration

1 code implementation24 Mar 2020 Cong Gao, Xingtong Liu, Wenhao Gu, Benjamin Killeen, Mehran Armand, Russell Taylor, Mathias Unberath

We propose a novel Projective Spatial Transformer module that generalizes spatial transformers to projective geometry, thus enabling differentiable volume rendering.

Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment

1 code implementation18 Mar 2020 Xingtong Liu, Maia Stiber, Jindan Huang, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath

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 understand the relationship between sinus anatomy and surgical outcomes.

3D Reconstruction Structure from Motion

From Perspective X-ray Imaging to Parallax-Robust Orthographic Stitching

no code implementations5 Mar 2020 Javad Fotouhi, Xingtong Liu, Mehran Armand, Nassir Navab, Mathias Unberath

Stitching images acquired under perspective projective geometry is a relevant topic in computer vision with multiple applications ranging from smartphone panoramas to the construction of digital maps.

Image Stitching

Extremely Dense Point Correspondences using a Learned Feature Descriptor

1 code implementation CVPR 2020 Xingtong Liu, Yiping Zheng, Benjamin Killeen, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath

In direct comparison to recent local and dense descriptors on an in-house sinus endoscopy dataset, we demonstrate that our proposed dense descriptor can generalize to unseen patients and scopes, thereby largely improving the performance of Structure from Motion (SfM) in terms of model density and completeness.

3D Reconstruction Optical Flow Estimation +1

Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video

no code implementations6 Sep 2019 Xingtong Liu, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath

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.

3D Reconstruction Self-Supervised Learning

LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices

1 code implementation9 Mar 2019 Laura Fink, Sing Chun Lee, Jie Ying Wu, Xingtong Liu, Tianyu Song, Yordanka Stoyanova, Marc Stamminger, Nassir Navab, Mathias Unberath

With the increasing computational power of today's workstations, real-time physically-based rendering is within reach, rapidly gaining attention across a variety of domains.

Data Visualization Image Generation

Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods

1 code implementation20 Feb 2019 Xingtong Liu, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Austin Reiter, Russell H. Taylor, Mathias Unberath

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 shading.

Computed Tomography (CT) Depth Estimation +2

Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks

no code implementations31 Jul 2018 Cong Gao, Xingtong Liu, Michael Peven, Mathias Unberath, Austin Reiter

Our method results in a mean absolute error of 0. 814 N in the ex vivo study, suggesting that it may be a promising alternative to hardware based surgical force feedback in endoscopic procedures.

Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy

no code implementations25 Jun 2018 Xingtong Liu, Ayushi Sinha, Mathias Unberath, Masaru Ishii, Gregory Hager, Russell H. Taylor, Austin Reiter

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 shading.

Depth Estimation Self-Supervised Learning +1

Endoscopic navigation in the absence of CT imaging

no code implementations8 Jun 2018 Ayushi Sinha, Xingtong Liu, Austin Reiter, Masaru Ishii, Gregory D. Hager, Russell H. Taylor

Clinical examinations that involve endoscopic exploration of the nasal cavity and sinuses often do not have a reference image to provide structural context to the clinician.

Computed Tomography (CT)

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