7 citations · 14 across the 8 of their papers we have counts for
8 papers
LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration
Aditya Ranjan Dash, Ramy Battrawy, René Schuster +1
Advanced autonomous systems rely on multi-sensor fusion for safer and more robust perception. To enable effective fusion, calibrating directly from natural driving scenes (i.e., ta…
SF3D-RGB: Scene Flow Estimation from Monocular Camera and Sparse LiDAR
Rajai Alhimdiat, Ramy Battrawy, René Schuster +2
Scene flow estimation is an extremely important task in computer vision to support the perception of dynamic changes in the scene. For robust scene flow, learning-based approaches…
EgoFlowNet: Non-Rigid Scene Flow from Point Clouds with Ego-Motion Support
Ramy Battrawy, René Schuster, Didier Stricker
Recent weakly-supervised methods for scene flow estimation from LiDAR point clouds are limited to explicit reasoning on object-level. These methods perform multiple iterative optim…
RMS-FlowNet++: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds
Ramy Battrawy, René Schuster, Didier Stricker
The proposed RMS-FlowNet++ is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation that can operate on high-density point clouds. For hie…
RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds
Ramy Battrawy, René Schuster, Mohammad-Ali Nikouei Mahani +1
The proposed RMS-FlowNet is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation which can operate on point clouds of high density. For h…
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…
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…
LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images
Ramy Battrawy, René Schuster, Oliver Wasenmüller +2
We propose a new approach called LiDAR-Flow to robustly estimate a dense scene flow by fusing a sparse LiDAR with stereo images. We take the advantage of the high accuracy of LiDAR…