Markus Ikeda — accepted AI-conference papers

2026

MIND - Multi-Feature Implicit Neural Descriptors for Robotic Surface Processing of 3D Objects with Variations in Geometry

Anish Pratheepkumar, Christian Hartl-Nesic, Markus Ikeda, Fabian Widmoser, Andreas Pichler, Markus Vincze

ICRA 2026poster

The recent shift from mass production to mass personalization leads to a production environment in which workpieces have a high degree of geometric variations. The robotic process automation in such high-mix low-volume environments poses significant challenges since predetermined robot programs are …

Cited by 1 Source ↗Scholar ↗

2025

MIND - Multi-Feature Implicit Neural Descriptors for Robotic Surface Processing of 3D Objects With Variations in Geometry

Anish Pratheepkumar, Christian Hartl-Nesic, Markus Ikeda, Fabian Widmoser, Andreas Pichler, Markus Vincze

RA-L 2025

The recent shift from mass production to mass personalization leads to a production environment in which workpieces have a high degree of geometric variations. The robotic process automation in such high-mix low-volume environments poses significant challenges since predetermined robot programs are

Cited by 0 Source ↗Scholar ↗

2024

NRDF - Neural Region Descriptor Fields as Implicit ROI Representation for Robotic 3D Surface Processing

Anish Pratheepkumar, Markus Ikeda, Michael Hofmann, Fabian Widmoser, Andreas Pichler, Markus Vincze

IROS 2024

To automate 3D surface processing across diverse category-level objects it is imperative to represent process-related region of interest (P-ROI), which is not obtained with conventional keypoint or semantic part correspondences. To resolve this issue, we propose Neural Region Descriptor Fields (NRDF

Cited by 4 Source ↗code ↗Scholar ↗