604 citations · 829 across the 4 of their papers we have counts for
6 papers
Domain Adaptation of Learned Features for Visual Localization
Sungyong Baik, Hyo Jin Kim, Tianwei Shen +3
We tackle the problem of visual localization under changing conditions, such as time of day, weather, and seasons. Recent learned local features based on deep neural networks have…
TLIO: Tight Learned Inertial Odometry
Wenxin Liu, David Caruso, Eddy Ilg +5
In this work we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. Strap-down IMU measurements provide relative state estimates based on IMU…
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg +4
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a…
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
Osama Makansi, Eddy Ilg, Özgün Cicek +1
Future prediction is a fundamental principle of intelligence that helps plan actions and avoid possible dangers. As the future is uncertain to a large extent, modeling the uncertai…
End-to-End Learning of Video Super-Resolution with Motion Compensation
Osama Makansi, Eddy Ilg, Thomas Brox
Learning approaches have shown great success in the task of super-resolving an image given a low resolution input. Video super-resolution aims for exploiting additionally the infor…
FlowNet: Learning Optical Flow with Convolutional Networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg +6
Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation ha…