most citedEnd-to-End Defect Detection in Automated Fiber Placement Based on Artificially Generated Data

17 citations · 30 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CV2020

BlendTorch: A Real-Time, Adaptive Domain Randomization Library

Christoph Heindl, Lukas Brunner, Sebastian Zambal +1

Solving complex computer vision tasks by deep learning techniques relies on large amounts of (supervised) image data, typically unavailable in industrial environments. The lack of…

cs.LG2020★ 1 cited

Graph Neural Networks for Node-Level Predictions

Christoph Heindl

The success of deep learning has revolutionized many fields of research including areas of computer vision, text and speech processing. Enormous research efforts have led to numero…

cs.CV2019

Enhanced Human-Machine Interaction by Combining Proximity Sensing with Global Perception

Christoph Heindl, Markus Ikeda, Gernot Stübl +2

The raise of collaborative robotics has led to wide range of sensor technologies to detect human-machine interactions: at short distances, proximity sensors detect nontactile gestu…

cs.CV2019★ 17 cited

End-to-End Defect Detection in Automated Fiber Placement Based on Artificially Generated Data

Sebastian Zambal, Christoph Heindl, Christian Eitzinger +1

Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and f…

cs.CV2019★ 3 cited

Metric Pose Estimation for Human-Machine Interaction Using Monocular Vision

Christoph Heindl, Markus Ikeda, Gernot Stübl +2

The rapid growth of collaborative robotics in production requires new automation technologies that take human and machine equally into account. In this work, we describe a monocula…

cs.CV2019

Learning to Predict Robot Keypoints Using Artificially Generated Images

Christoph Heindl, Sebastian Zambal, Josef Scharinger

This work considers robot keypoint estimation on color images as a supervised machine learning task. We propose the use of probabilistically created renderings to overcome the lack…

cs.CV2019

Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time

Christoph Heindl, Thomas Pönitz, Gernot Stübl +2

Commodity RGB-D sensors capture color images along with dense pixel-wise depth information in real-time. Typical RGB-D sensors are provided with a factory calibration and exhibit e…

cs.CV2019★ 5 cited

Large Area 3D Human Pose Detection Via Stereo Reconstruction in Panoramic Cameras

Christoph Heindl, Thomas Pönitz, Andreas Pichler +1

We propose a novel 3D human pose detector using two panoramic cameras. We show that transforming fisheye perspectives to rectilinear views allows a direct application of two-dimens…

cs.CV2019★ 4 cited

3D Robot Pose Estimation from 2D Images

Christoph Heindl, Sebastian Zambal, Thomas Ponitz +2

This paper considers the task of locating articulated poses of multiple robots in images. Our approach simultaneously infers the number of robots in a scene, identifies joint locat…