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Found 7 papers, 5 papers with code
Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data
To tackle this issue, our study presents a benchmark for assessing the robustness of endoscopic depth estimation models.
Registering Neural 4D Gaussians for Endoscopic Surgery
In this paper, we propose a novel strategy for dynamic surgical neural scene registration.
Head Pose Estimation and 3D Neural Surface Reconstruction via Monocular Camera in situ for Navigation and Safe Insertion into Natural Openings
As the significance of simulation in medical care and intervention continues to grow, it is anticipated that a simplified and low-cost platform can be set up to execute personalized diagnoses and treatments.
EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera
We propose Endoscopic Depth Any Camera (EndoDAC) which is an efficient self-supervised depth estimation framework that adapts foundation models to endoscopic scenes.
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes.
Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery
There is clear evidence in the results that zero-shot prediction on pre-trained weights in computer vision datasets or naive fine-tuning is not sufficient to use the foundation model in the surgical domain directly.
Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation
Therefore, Temporal Feature Affinity Learning (TFAL) is devised to indicate possible noisy labels by evaluating the affinity between pixels in two adjacent frames.
Ranked #2 on
Video Polyp Segmentation
on SUN-SEG-Easy
