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Found 9 papers, 6 papers with code
EndoMetric: Near-Light Monocular Metric Scale Estimation in Endoscopy
Geometric reconstruction and SLAM with endoscopic images have advanced significantly in recent years.
Topological SLAM in colonoscopies leveraging deep features and topological priors
We introduce ColonSLAM, a system that combines classical multiple-map metric SLAM with deep features and topological priors to create topological maps of the whole colon.
CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in real human endoscopy
To overcome the limitations of ORB-SLAM3, we use SIFT instead of ORB features and replace the DBoW2 direct index with the more computationally demanding brute-force matching, being able to successfully match images separated in time for relocation and map merging.
LightNeuS: Neural Surface Reconstruction in Endoscopy using Illumination Decline
It comes from the endoscope's light sources and decays with the inverse of the squared distance to the surface.
SimCol3D -- 3D Reconstruction during Colonoscopy Challenge
We show that depth prediction from synthetic colonoscopy images is robustly solvable, while pose estimation remains an open research question.
EndoMapper dataset of complete calibrated endoscopy procedures
Computer-assisted systems are becoming broadly used in medicine.
Medical Image Retrieval
Simultaneous Localization and Mapping
SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenes
Conventional SLAM techniques strongly rely on scene rigidity to solve data association, ignoring dynamic parts of the scene.
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
Compared with visual odometry systems that only use information from the last few seconds, ORB-SLAM3 is the first system able to reuse in all the algorithm stages all previous information.
Robotics
Inertial-Only Optimization for Visual-Inertial Initialization
We formulate for the first time visual-inertial initialization as an optimal estimation problem, in the sense of maximum-a-posteriori (MAP) estimation.
Robotics
