Christian Unger — accepted AI-conference papers

2021

Binary-LoRAX: Low-Latency Runtime Adaptable XNOR Classifier for Semi-Autonomous Grasping with Prosthetic Hands

Nael Fasfous, Manoj-Rohit Vemparala, Alexander Frickenstein, Mohamed Badawy, Felix Hundhausen, Julian Höfer +6

ICRA 2021poster

Intelligent, semi-autonomous prostheses take ad-vantage of combining autonomous functions and traditional myoelectric control. With the help of visual and environment sensors, intelligent prostheses achieve a level of autonomy which relieves the user from generating elaborate electromyographic (EMG)…

Cited by 11 Source ↗Scholar ↗

2020

Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving

Alexander Frickenstein, Manoj-Rohit Vemparala, Jakob Mayr, Naveen-Shankar Nagaraja, Christian Unger, Federico Tombari +1

ICRA 2020poster

Driveable area detection is a key component for various applications in the field of autonomous driving (AD), such as ground-plane detection, obstacle detection and maneuver planning. Additionally, bulky and over-parameterized networks can be easily forgone and replaced with smaller networks for fas…

Cited by 28 Source ↗Scholar ↗

2019

SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks

Rene Schuster, Oliver Wasenmuller, Christian Unger, Didier Stricker

CVPR 2019oral

Dense pixel matching is important for many computer vision tasks such as disparity and flow estimation. We present a robust, unified descriptor network that considers a large context region with high spatial variance. Our network has a very large receptive field and avoids striding layers to mainta…

Cited by 53 PDF ↗code ↗Scholar ↗

2018

Self-Supervised Learning of the Drivable Area for Autonomous Vehicles

Jakob Mayr, Christian Unger, Federico Tombari

IROS 2018poster

We propose a new approach for generating training data for the task of drivable area segmentation with deep neural networks (DNN). The impressive progress of deep learning in recent years demonstrated a superior performance of DNNs over traditional machine learning and deterministic algorithms for v…

Cited by 25 Source ↗Scholar ↗