26 citations · 71 across the 39 of their papers we have counts for
6 papers · 1 filter
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…
HPERL: 3D Human Pose Estimation from RGB and LiDAR
Michael Fürst, Shriya T. P. Gupta, René Schuster +2
In-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driv…
DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR
Rishav, Ramy Battrawy, René Schuster +2
Scene flow is the dense 3D reconstruction of motion and geometry of a scene. Most state-of-the-art methods use a pair of stereo images as input for full scene reconstruction. These…
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…
ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching
Rishav, René Schuster, Ramy Battrawy +2
Dense pixel matching is required for many computer vision algorithms such as disparity, optical flow or scene flow estimation. Feature Pyramid Networks (FPN) have proven to be a su…