Search Results for author: Eddy Ilg

Found 24 papers, 12 papers with code

latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction

no code implementations24 Mar 2024 Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen

We present latentSplat, a method to predict semantic Gaussians in a 3D latent space that can be splatted and decoded by a light-weight generative 2D architecture.

3D Reconstruction

Recent Trends in 3D Reconstruction of General Non-Rigid Scenes

no code implementations22 Mar 2024 Raza Yunus, Jan Eric Lenssen, Michael Niemeyer, Yiyi Liao, Christian Rupprecht, Christian Theobalt, Gerard Pons-Moll, Jia-Bin Huang, Vladislav Golyanik, Eddy Ilg

Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision.

3D Reconstruction Navigate

Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids

no code implementations26 Dec 2023 Cameron Braunstein, Eddy Ilg, Vladislav Golyanik

Our approach is hybrid (i. e., quantum-classical) and is compatible with modern D-Wave quantum annealers, i. e., it includes a quadratic unconstrained binary optimization (QUBO) objective.

Combinatorial Optimization Stereo Matching

Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation

1 code implementation21 Dec 2023 Philipp Schröppel, Christopher Wewer, Jan Eric Lenssen, Eddy Ilg, Thomas Brox

However, none of the existing models enable disentangled generation to control the shape and appearance separately.

Disentanglement

Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction

no code implementations2 Dec 2023 Devikalyan Das, Christopher Wewer, Raza Yunus, Eddy Ilg, Jan Eric Lenssen

In this work, we introduce Neural Parametric Gaussians (NPGs) to take on this challenge by imposing a two-stage approach: first, we fit a low-rank neural deformation model, which then is used as regularization for non-rigid reconstruction in the second stage.

Object Object Reconstruction

SimNP: Learning Self-Similarity Priors Between Neural Points

no code implementations ICCV 2023 Christopher Wewer, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen

(1) We design the first neural point representation on a category level by utilizing the concept of coherent point clouds.

3D Object Reconstruction Object

ERF: Explicit Radiance Field Reconstruction From Scratch

no code implementations28 Feb 2022 Samir Aroudj, Steven Lovegrove, Eddy Ilg, Tanner Schmidt, Michael Goesele, Richard Newcombe

Robustly reconstructing such a volumetric scene model with millions of unknown variables from registered scene images only is a highly non-convex and complex optimization problem.

3D Reconstruction

NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning

no code implementations CVPR 2022 Tony Ng, Hyo Jin Kim, Vincent Lee, Daniel DeTone, Tsun-Yi Yang, Tianwei Shen, Eddy Ilg, Vassileios Balntas, Krystian Mikolajczyk, Chris Sweeney

We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors.

Camera Localization Image Reconstruction

Domain Adaptation of Learned Features for Visual Localization

no code implementations21 Aug 2020 Sungyong Baik, Hyo Jin Kim, Tianwei Shen, Eddy Ilg, Kyoung Mu Lee, Chris Sweeney

We tackle the problem of visual localization under changing conditions, such as time of day, weather, and seasons.

Domain Adaptation Visual Localization

TLIO: Tight Learned Inertial Odometry

no code implementations6 Jul 2020 Wenxin Liu, David Caruso, Eddy Ilg, Jing Dong, Anastasios I. Mourikis, Kostas Daniilidis, Vijay Kumar, Jakob Engel

We show that our network, trained with pedestrian data from a headset, can produce statistically consistent measurement and uncertainty to be used as the update step in the filter, and the tightly-coupled system outperforms velocity integration approaches in position estimates, and AHRS attitude filter in orientation estimates.

Position

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

no code implementations20 Aug 2018 Osama Makansi, Eddy Ilg, Thomas Brox

The latter can be used as proxy-ground-truth to train a network on real-world data and to adapt it to specific domains of interest.

Optical Flow Estimation

Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow

1 code implementation ECCV 2018 Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox

Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology.

Optical Flow Estimation

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

1 code implementation19 Jan 2018 Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox

The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations.

Optical Flow Estimation

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

no code implementations3 Jul 2017 Osama Makansi, Eddy Ilg, Thomas Brox

We analyze the usage of optical flow for video super-resolution and find that common off-the-shelf image warping does not allow video super-resolution to benefit much from optical flow.

Motion Compensation Optical Flow Estimation +1

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