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Found 7 papers, 1 papers with code
DaReNeRF: Direction-aware Representation for Dynamic Scenes
However, the straightforward decomposition of 4D dynamic scenes into multiple 2D plane-based representations proves insufficient for re-rendering high-fidelity scenes with complex motions.
WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters
In this work, we aimed to build a self-supervised depth and ego-motion estimation system which can predict not only accurate depth maps and camera pose, but also camera intrinsic parameters.
SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)
The accurate reconstruction of surgical scenes from surgical videos is critical for various applications, including intraoperative navigation and image-guided robotic surgery automation.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label
AI-assisted surgeries have drawn the attention of the medical image research community due to their real-world impact on improving surgery success rates.
Self-Supervised Surgical Instrument 3D Reconstruction from a Single Camera Image
An accurate 3D surgical instrument model is a prerequisite for precise predictions of the pose and depth of the instrument.
Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation
In contrast to the previous state-of-the-art, we introduce Min-Max Similarity (MMS), a contrastive learning form of dual-view training by employing classifiers and projectors to build all-negative, and positive and negative feature pairs, respectively, to formulate the learning as solving a MMS problem.
