16 citations · 102 across the 53 of their papers we have counts for
9 papers · 1 filter
Automated Scene Flow Data Generation for Training and Verification
Oliver Wasenmüller, René Schuster, Didier Stricker +6
Scene flow describes the 3D position as well as the 3D motion of each pixel in an image. Such algorithms are the basis for many state-of-the-art autonomous or automated driving fun…
Dense Scene Flow from Stereo Disparity and Optical Flow
René Schuster, Oliver Wasenmüller, Didier Stricker
Scene flow describes 3D motion in a 3D scene. It can either be modeled as a single task, or it can be reconstructed from the auxiliary tasks of stereo depth and optical flow estima…
DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth
Jameel Malik, Ahmed Elhayek, Fabrizio Nunnari +4
Articulated hand pose and shape estimation is an important problem for vision-based applications such as augmented reality and animation. In contrast to the existing methods which…
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
Kripasindhu Sarkar, Basavaraj Hampiholi, Kiran Varanasi +1
We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs. We represent 3D shapes as multi-layered height-maps (MLH) where at each grid locatio…
FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation
René Schuster, Christian Bailer, Oliver Wasenmüller +1
Optical Flow algorithms are of high importance for many applications. Recently, the Flow Field algorithm and its modifications have shown remarkable results, as they have been eval…
Fast Feature Extraction with CNNs with Pooling Layers
Christian Bailer, Tewodros Habtegebrial, Kiran varanasi +1
In recent years, many publications showed that convolutional neural network based features can have a superior performance to engineered features. However, not much effort was take…