Search Results for author:
Found 14 papers, 9 papers with code
SAGE: SLAM with Appearance and Geometry Prior for Endoscopy
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 of the observed anatomy from a monocular endoscopic video.
Neighborhood Normalization for Robust Geometric Feature Learning
Extracting geometric features from 3D models is a common first step in applications such as 3D registration, tracking, and scene flow estimation.
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers
Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth.
Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration
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
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.
From Perspective X-ray Imaging to Parallax-Robust Orthographic Stitching
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.
Extremely Dense Point Correspondences using a Learned Feature Descriptor
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.
2018 Robotic Scene Segmentation Challenge
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 kinematics and instrument CAD models.
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video
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.
LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices
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.
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods
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.
Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks
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
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.
Endoscopic navigation in the absence of CT imaging
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.
