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Found 11 papers, 4 papers with code
HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management
The first step in automation of hemostasis management is detection of blood in the surgical field.
BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fields
We demonstrate this approach on endoscopic surgical scenes from robotic surgery.
SuPerPM: A Large Deformation-Robust Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data
Thus, for tuning the learning model, we gather endoscopic data of soft tissue being manipulated by a surgical robot and then establish correspondences between point clouds at different time points to serve as ground truth.
BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives
Implicit neural representations have become pivotal in robotic perception, enabling robots to comprehend 3D environments from 2D images.
Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer
Solving the camera-to-robot pose is a fundamental requirement for vision-based robot control, and is a process that takes considerable effort and cares to make accurate.
Ranked #2 on
Robot Pose Estimation
on DREAM-dataset
Image-based Pose Estimation and Shape Reconstruction for Robot Manipulators and Soft, Continuum Robots via Differentiable Rendering
We demonstrate that our method of using geometrical shape primitives can achieve high accuracy in shape reconstruction for a soft continuum robot and pose estimation for a robot manipulator.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking
In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation.
Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer
The physical world experiments show how the proposed method can be applied to the wide-breadth of robotic applications that require visual feedback, such as camera-to-robot calibration, robotic tool tracking, and end-effector pose estimation.
SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction
Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue.
SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics
In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control.
