18 citations · 20 across the 8 of their papers we have counts for
9 papers
L2PF -- Learning to Prune Faster
Manoj-Rohit Vemparala, Nael Fasfous, Alexander Frickenstein +6
Various applications in the field of autonomous driving are based on convolutional neural networks (CNNs), especially for processing camera data. The optimization of such CNNs is a…
MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow
René Schuster, Christian Unger, Didier Stricker
Contrary to the ongoing trend in automotive applications towards usage of more diverse and more sensors, this work tries to solve the complex scene flow problem under a monocular c…
A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions
René Schuster, Christian Unger, Didier Stricker
Motion estimation is one of the core challenges in computer vision. With traditional dual-frame approaches, occlusions and out-of-view motions are a limiting factor, especially in…
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation
René Schuster, Oliver Wasenmüller, Christian Unger +1
Interpolation of sparse pixel information towards a dense target resolution finds its application across multiple disciplines in computer vision. State-of-the-art interpolation of…
ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks
Alexander Frickenstein, Manoj-Rohit Vemparala, Nael Fasfous +4
Closing the gap between the hardware requirements of state-of-the-art convolutional neural networks and the limited resources constraining embedded applications is the next big cha…
Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving
Alexander Frickenstein, Manoj Rohit Vemparala, Jakob Mayr +4
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 planni…
An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching
René Schuster, Oliver Wasenmüller, Christian Unger +1
Training a deep neural network is a non-trivial task. Not only the tuning of hyperparameters, but also the gathering and selection of training data, the design of the loss function…
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks
René Schuster, Oliver Wasenmüller, Christian Unger +1
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 conte…
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation
René Schuster, Oliver Wasenmüller, Christian Unger +2
State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense…