3 papers
cs.CV2026
PIRATR: Parametric Object Inference for Robotic Applications with Transformers in 3D Point Clouds
Michael Schwingshackl, Fabio F. Oberweger, Mario Niedermeyer +2
We present PIRATR, an end-to-end 3D object detection framework for robotic use cases in point clouds. Extending PI3DETR, our method streamlines parametric 3D object detection by jo…
cs.CV2026
PI3DETR: Parametric Instance Detection of 3D Point Cloud Edges With a Geometry-Aware 3DETR
Fabio F. Oberweger, Michael Schwingshackl, Vanessa Staderini
We present PI3DETR, an end-to-end framework that directly predicts 3D parametric curve instances from raw point clouds, avoiding the intermediate representations and multi-stage pr…
cs.CV2025
Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks
Michael Schwingshackl, Fabio Francisco Oberweger, Markus Murschitz
This paper proposes a novel approach to few-shot semantic segmentation for machinery with multiple parts that exhibit spatial and hierarchical relationships. Our method integrates…