activity
20172024
most cited2017 Robotic Instrument Segmentation Challenge

57 citations · 93 across the 6 of their papers we have counts for

collaborators
Showing cs.CVShow all

9 papers · 1 filter

cs.CV2026

SCARED-C: Corrected Camera Poses for Endoscopic Depth Estimation

John J. Han, Adam Schmidt, Max Allan +2

The SCARED dataset is a widely used benchmark for endoscopic depth estimation, offering ground-truth 3D reconstructions captured with a structured light sensor. However, the depth…

cs.CV2024

Online 3D reconstruction and dense tracking in endoscopic videos

Michel Hayoz, Christopher Hahne, Thomas Kurmann +5

3D scene reconstruction from stereo endoscopic video data is crucial for advancing surgical interventions. In this work, we present an online framework for online, dense 3D scene r…

cs.CV202124 cited

Stereo Correspondence and Reconstruction of Endoscopic Data Challenge

Max Allan, Jonathan Mcleod, Congcong Wang +21

The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. The task was to perform…

cs.CV20203 cited

daVinciNet: Joint Prediction of Motion and Surgical State in Robot-Assisted Surgery

Yidan Qin, Seyedshams Feyzabadi, Max Allan +2

This paper presents a technique to concurrently and jointly predict the future trajectories of surgical instruments and the future state(s) of surgical subtasks in robot-assisted s…

cs.CV20209 cited

Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources

Yidan Qin, Sahba Aghajani Pedram, Seyedshams Feyzabadi +4

Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an ob…

cs.CV2020

2018 Robotic Scene Segmentation Challenge

Max Allan, Satoshi Kondo, Sebastian Bodenstedt +38

In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward ki…