ArticleSensors (Basel, Switzerland)2023
Real-Time Dense Reconstruction with Binocular Endoscopy Based on StereoNet and ORB-SLAM.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 17 citations in OpenAlex.
- Intraoperative patient-specific volumetric reconstruction and 3D visualization for laparoscopic liver surgery.Healthcare technology letters · 2024Article
- ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry.Sensors (Basel, Switzerland) · 2024Article
- Advances in Real-Time 3D Reconstruction for Medical Endoscopy.Journal of imaging · 2024Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Binocular endoscopy is gradually becoming the future of minimally invasive surgery (MIS) thanks to the development of stereo vision. However, some problems still exist, such as the low reconstruction accuracy, small surgical field, and low computational efficiency. To solve these problems, we designed a framework for real-time dense reconstruction in binocular endoscopy scenes. First, we obtained the initial disparity map using an SGBM algorithm and proposed the disparity confidence map as a dataset to provide StereoNet training. Then, based on the depth map predicted by StereoNet, the corresponding left image of each depth map was input into the Oriented Fast and Brief-Simultaneous Localization and Mapping (ORB-SLAM) framework using an RGB-D camera to realize the real-time dense reconstruction of the binocular endoscopy scene. The proposed algorithm was verified in the stomach phantom and a real pig stomach. Compared with the ground truth, the proposed algorithm's RMSE is 1.620 mm, and the number of effective points in the point cloud is 834,650, which is a significant improvement in the mapping ability compared with binocular SLAM and ensures the real-time performance of the algorithm while performing dense reconstruction. The effectiveness of the proposed algorithm is verified.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.