Eddy Ilg | DeepAI

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∙ 09/07/2023

SimNP: Learning Self-Similarity Priors Between Neural Points

Existing neural field representations for 3D object reconstruction eithe...

0 Christopher Wewer, et al. ∙

∙ 06/05/2022

Recurrent Video Restoration Transformer with Guided Deformable Attention

Video restoration aims at restoring multiple high-quality frames from mu...

4 Jingyun Liang, et al. ∙

∙ 02/28/2022

ERF: Explicit Radiance Field Reconstruction From Scratch

We propose a novel explicit dense 3D reconstruction approach that proces...

13 Samir Aroudj, et al. ∙

∙ 12/23/2021

NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning

In the light of recent analyses on privacy-concerning scene revelation f...

10 Tony Ng, et al. ∙

∙ 05/09/2021

Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors

As autonomous driving and augmented reality evolve, a practical concern ...

0 Deeksha Dangwal, et al. ∙

∙ 08/21/2020

Domain Adaptation of Learned Features for Visual Localization

We tackle the problem of visual localization under changing conditions, ...

0 Sungyong Baik, et al. ∙

∙ 07/06/2020

TLIO: Tight Learned Inertial Odometry

In this work we propose a tightly-coupled Extended Kalman Filter framewo...

0 Wenxin Liu, et al. ∙

∙ 03/24/2020

Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction

Efficiently reconstructing complex and intricate surfaces at scale is a ...

10 Rohan Chabra, et al. ∙

∙ 06/09/2019

Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction

Future prediction is a fundamental principle of intelligence that helps ...

8 Osama Makansi, et al. ∙

∙ 08/20/2018

FusionNet and AugmentedFlowNet: Selective Proxy Ground Truth for Training on Unlabeled Images

Recent work has shown that convolutional neural networks (CNNs) can be u...

0 Osama Makansi, et al. ∙

∙ 08/06/2018

Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation

Occlusions play an important role in disparity and optical flow estimati...

2 Eddy Ilg, et al. ∙

∙ 02/20/2018

Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks

Recent work has shown that optical flow estimation can be formulated as ...

0 Eddy Ilg, et al. ∙

∙ 01/19/2018

What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?

The finding that very large networks can be trained efficiently and reli...

0 Nikolaus Mayer, et al. ∙

∙ 07/03/2017

End-to-End Learning of Video Super-Resolution with Motion Compensation

Learning approaches have shown great success in the task of super-resolv...

0 Osama Makansi, et al. ∙

∙ 03/28/2017

Lucid Data Dreaming for Multiple Object Tracking

Convolutional networks reach top quality in pixel-level object tracking ...

0 Anna Khoreva, et al. ∙

∙ 12/07/2016

DeMoN: Depth and Motion Network for Learning Monocular Stereo

In this paper we formulate structure from motion as a learning problem. ...

0 Benjamin Ummenhofer, et al. ∙

∙ 12/06/2016

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

The FlowNet demonstrated that optical flow estimation can be cast as a l...

0 Eddy Ilg, et al. ∙

∙ 12/07/2015

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Recent work has shown that optical flow estimation can be formulated as ...

0 Nikolaus Mayer, et al. ∙

∙ 04/26/2015

FlowNet: Learning Optical Flow with Convolutional Networks

Convolutional neural networks (CNNs) have recently been very successful ...

0 Philipp Fischer, et al. ∙

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