Stefan Ainetter — accepted AI-conference papers

2025

PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction

Sinisa Stekovic, Arslan Artykov, Stefan Ainetter, Mattia D'Urso, Friedrich Fraundorfer

CVPR 2025poster

We propose PyTorchGeoNodes, a differentiable module for reconstructing 3D objects and their parameters from images using interpretable shape programs. Unlike traditional CAD model retrieval, shape programs allow reasoning about semantic parameters, editing, and a low memory footprint. Despite their…

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2021

End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB

Stefan Ainetter, Friedrich Fraundorfer

ICRA 2021poster

In this work, we introduce a novel, end-to-end trainable CNN-based architecture to deliver high quality results for grasp detection suitable for a parallel-plate gripper, and semantic segmentation. Utilizing this, we propose a novel refinement module that takes advantage of previously calculated gra…

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