70 citations · 104 across the 11 of their papers we have counts for
15 papers
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data
David Schinagl, Georg Krispel, Horst Possegger +2
While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we pr…
Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
Antonio Pepe, Jan Egger, Marina Codari +7
Objective: Surveillance imaging of chronic aortic diseases, such as dissections, relies on obtaining and comparing cross-sectional diameter measurements at predefined aortic landma…
Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild
Alexander Grabner, Yaming Wang, Peizhao Zhang +5
We present a novel 3D pose refinement approach based on differentiable rendering for objects of arbitrary categories in the wild. In contrast to previous methods, we make two main…
ALCN: Adaptive Local Contrast Normalization
Mahdi Rad, Peter M. Roth, Vincent Lepetit
To make Robotics and Augmented Reality applications robust to illumination changes, the current trend is to train a Deep Network with training images captured under many different…
Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs
Marcus D. Bloice, Peter M. Roth, Andreas Holzinger
In this paper a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs. A convolutional neural network was trained with images…
Patch augmentation: Towards efficient decision boundaries for neural networks
Marcus D. Bloice, Peter M. Roth, Andreas Holzinger
In this paper we propose a new augmentation technique, called patch augmentation, that, in our experiments, improves model accuracy and makes networks more robust to adversarial at…
L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization
Mina Basirat, Peter M. Roth
Deep neural networks paved the way for significant improvements in image visual categorization during the last years. However, even though the tasks are highly varying, differing i…
Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation
Martin Hirzer, Peter M. Roth, Vincent Lepetit
We propose a novel method to efficiently estimate the spatial layout of a room from a single monocular RGB image. As existing approaches based on low-level feature extraction, foll…
Location Field Descriptors: Single Image 3D Model Retrieval in the Wild
Alexander Grabner, Peter M. Roth, Vincent Lepetit
We present Location Field Descriptors, a novel approach for single image 3D model retrieval in the wild. In contrast to previous methods that directly map 3D models and RGB images…
GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild
Alexander Grabner, Peter M. Roth, Vincent Lepetit
We present a joint 3D pose and focal length estimation approach for object categories in the wild. In contrast to previous methods that predict 3D poses independently of the focal…
Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data
Christina Gsaxner, Peter M. Roth, Jürgen Wallner +1
We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an i…
The Quest for the Golden Activation Function
Mina Basirat, Peter M. Roth
Deep Neural Networks have been shown to be beneficial for a variety of tasks, in particular allowing for end-to-end learning and reducing the requirement for manual design decision…
3D Pose Estimation and 3D Model Retrieval for Objects in the Wild
Alexander Grabner, Peter M. Roth, Vincent Lepetit
We propose a scalable, efficient and accurate approach to retrieve 3D models for objects in the wild. Our contribution is twofold. We first present a 3D pose estimation approach fo…
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
Andreas Holzinger, Bernd Malle, Peter Kieseberg +4
Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the…
ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation
Mahdi Rad, Peter M. Roth, Vincent Lepetit
To be robust to illumination changes when detecting objects in images, the current trend is to train a Deep Network with training images captured under many different lighting cond…