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Andreas Pichler

5 accepted 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 0Source ↗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 0Source ↗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 4Source ↗code ↗Scholar ↗
2018

Towards a Context Enhanced Framework for Multi Object Tracking in Human Robot Collaboration

Sharath Chandra Akkaladevi, Matthias Plasch, Christian Eitzinger, Andreas Pichler, Bernhard Rinner
IROS 2018poster

In a goal-oriented Human Robot Collaborative (HRC) scenario, where the goal is to complete an assembly process, a robust object tracker might not necessarily fulfill its functional role due to the dynamic nature of HRC. Moreover, for an efficient HRC, the functional role of the object tacker should…

Cited by 11Source ↗Scholar ↗
2016

Tracking multiple rigid symmetric and non-symmetric objects in real-time using depth data

Sharath Chandra Akkaladevi, Martin Ankerl, Christoph Heindl, Andreas Pichler
ICRA 2016

In this paper, a robust, real-time object tracking approach capable of dealing with multiple symmetric and non-symmetric objects in a real-time requirement setting is proposed. The approach relies only on depth data to track multiple objects in a dynamic environment and uses random-forest based lear

Cited by 28Source ↗Scholar ↗