Stefan Hinterstoisser — accepted AI-conference papers

2026

3D-Object Perception Transformer (3PT)

Agastya Kalra, Tim Salzmann, Guy Stoppi, Dmitrii Marin, Rishav Agarwal, Vage Taamazyan +7

CVPR 2026

Current approaches to zero-shot 3D-object perception typically rely on ensembles of frozen foundation models. This limits deep object understanding and cross-domain generalization, making performance inadequate for real-world deployment. The 3D-Object Perception Transformer (3PT) addresses this limi

Cited by 0 Source ↗Scholar ↗

2018

Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation

Kuan Fang, Yunfei Bai, Stefan Hinterstoisser, Silvio Savarese, Mrinal Kalakrishnan

ICRA 2018poster

Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network t…

Cited by 135 Source ↗Scholar ↗