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Found 6 papers, 1 papers with code
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos
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.
A Surface-normal Based Neural Framework for Colonoscopy Reconstruction
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.
ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction
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.
Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces
In this work we focus on the lighting problem in colonoscopy videos.
Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth
Deep learning-based, single-view depth estimation methods have recently shown highly promising results.
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video
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.
