activity
20232026
most citedLOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images

2 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.RO2026

Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy

Ethan Kilmer, Pit Henrich, Jiawei Ge +13

Autonomous soft-tissue cancer surgery has been limited to interventions on organ surfaces, because current systems cannot perceive and adapt to anatomy once it deforms or is cut. W…

cs.RO2026

Comparing Commercial Depth Sensor Accuracy for Medical Applications

Pit Henrich, Maximilian Weiherer, Franziska Hansen +2

Depth estimation has numerous medical and surgical applications. We benchmark four depth sensors on a porcine bone specimen, a porcine belly specimen, and a silicone kidney phantom…

cs.RO2026

Point Cloud Segmentation for Autonomous Clip Positioning in Laparoscopic Cholecystectomy on a Phantom

Balázs Gyenes, Nikolai Franke, Paul Maria Scheikl +5

High-risk applications in robotics, such as robot-assisted surgery, present unique challenges. These systems must be both highly precise and interpretable in order to be deployed i…

cs.RO2024

Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks

Pit Henrich, Jiawei Liu, Jiawei Ge +5

To track tumors during surgery, information from preoperative CT scans is used to determine their position. However, as the surgeon operates, the tumor may be deformed which presen…

cs.RO2024

LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions

Pit Henrich, Franziska Mathis-Ullrich, Paul Maria Scheikl

Accurately determining the shape of deformable objects and the location of their internal structures is crucial for medical tasks that require precise targeting, such as robotic bi…

cs.CV2024★ 2 cited

LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images

Pit Henrich, Franziska Mathis-Ullrich

We introduce a novel approach for the precise localization of 67 anatomical structures from single depth images captured from the exterior of the human body. Our method uses a mult…

cs.CV2023

Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud

Pit Henrich, Balázs Gyenes, Paul Maria Scheikl +2

In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object. We thus require a syste…