Search Results for author: Daniel Cremers

Found 185 papers, 79 papers with code

Beyond In-Domain Scenarios: Robust Density-Aware Calibration

no code implementations10 Feb 2023 Christian Tomani, Futa Waseda, Yuesong Shen, Daniel Cremers

While existing post-hoc calibration methods achieve impressive results on in-domain test datasets, they are limited by their inability to yield reliable uncertainty estimates in domain-shift and out-of-domain (OOD) scenarios.

Semidefinite Relaxations for Robust Multiview Triangulation

1 code implementation26 Jan 2023 Linus Härenstam-Nielsen, Niclas Zeller, Daniel Cremers

We propose an approach based on convex relaxations for certifiably optimal robust multiview triangulation.

Behind the Scenes: Density Fields for Single View Reconstruction

1 code implementation18 Jan 2023 Felix Wimbauer, Nan Yang, Christian Rupprecht, Daniel Cremers

Currently, neural radiance fields (NeRFs) can capture true 3D including color but are too complex to be generated from a single image.

Depth Estimation Depth Prediction +1

Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information

1 code implementation6 Jan 2023 Dekai Zhu, Qadeer Khan, Daniel Cremers

This is done by training a neural network which takes the state and intent of the multiple vehicles to predict their future trajectory.

Trajectory Prediction

4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions

no code implementations31 Dec 2022 Patrick Wenzel, Nan Yang, Rui Wang, Niclas Zeller, Daniel Cremers

In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset.

Autonomous Driving Benchmarking +2

Masked Event Modeling: Self-Supervised Pretraining for Event Cameras

no code implementations20 Dec 2022 Simon Klenk, David Bonello, Lukas Koestler, Daniel Cremers

Event cameras offer the capacity to asynchronously capture brightness changes with low latency, high temporal resolution, and high dynamic range.

Event-based vision

SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering

no code implementations9 Dec 2022 Mohammed Brahimi, Bjoern Haefner, Tarun Yenamandra, Bastian Goldluecke, Daniel Cremers

We propose an end-to-end inverse rendering pipeline called SupeRVol that allows us to recover 3D shape and material parameters from a set of color images in a super-resolution manner.


G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors

no code implementations6 Dec 2022 Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers

We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence.

PRISM: Probabilistic Real-Time Inference in Spatial World Models

no code implementations6 Dec 2022 Atanas Mirchev, Baris Kayalibay, Ahmed Agha, Patrick van der Smagt, Daniel Cremers, Justin Bayer

We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception.

Bayesian Inference

PVT3D: Point Voxel Transformers for Place Recognition from Sparse Lidar Scans

no code implementations22 Nov 2022 Yan Xia, Mariia Gladkova, Rui Wang, João F. Henriques, Daniel Cremers, Uwe Stilla

Training deep networks to match such scans presents a difficult trade-off: a higher spatial resolution of the network's intermediate representations is needed to perform fine-grained matching of subtle geometric features, but growing it too large makes the memory requirements infeasible.

A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs

1 code implementation27 Oct 2022 Hans Hao-Hsun Hsu, Yuesong Shen, Daniel Cremers

Current graph neural networks (GNNs) that tackle node classification on graphs tend to only focus on nodewise scores and are solely evaluated by nodewise metrics.

Node Classification Structured Prediction

Deep Combinatorial Aggregation

1 code implementation12 Oct 2022 Yuesong Shen, Daniel Cremers

In this work, we explore a combinatorial generalization of deep ensemble called deep combinatorial aggregation (DCA).

Image Classification

What Makes Graph Neural Networks Miscalibrated?

1 code implementation12 Oct 2022 Hans Hao-Hsun Hsu, Yuesong Shen, Christian Tomani, Daniel Cremers

Furthermore, based on the insights from this study, we design a novel calibration method named Graph Attention Temperature Scaling (GATS), which is tailored for calibrating graph neural networks.

Graph Attention Multi-class Classification

E-NeRF: Neural Radiance Fields from a Moving Event Camera

1 code implementation24 Aug 2022 Simon Klenk, Lukas Koestler, Davide Scaramuzza, Daniel Cremers

We also show that combining events and frames can overcome failure cases of NeRF estimation in scenarios where only a few input views are available without requiring additional regularization.

Semantic Self-adaptation: Enhancing Generalization with a Single Sample

1 code implementation10 Aug 2022 Sherwin Bahmani, Oliver Hahn, Eduard Zamfir, Nikita Araslanov, Daniel Cremers, Stefan Roth

In this work, we challenge this premise with a self-adaptive approach for semantic segmentation that adjusts the inference process to each input sample.

Domain Generalization Semantic Segmentation

Efficient and Flexible Sublabel-Accurate Energy Minimization

1 code implementation20 Jun 2022 Zhakshylyk Nurlanov, Daniel Cremers, Florian Bernard

While discrete optimization methods are able to give theoretical optimality guarantees, they can only handle a finite number of labels and therefore suffer from label discretization bias.

Image Denoising

Biologically Inspired Neural Path Finding

1 code implementation13 Jun 2022 Hang Li, Qadeer Khan, Volker Tresp, Daniel Cremers

The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses.

CHALLENGER: Training with Attribution Maps

no code implementations30 May 2022 Christian Tomani, Daniel Cremers

Regularization is key in deep learning, especially when training complex models on relatively small datasets.

Time Series Analysis

VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments

1 code implementation23 May 2022 Michael Schleiss, Fahmi Rouatbi, Daniel Cremers

Visual Place Recognition and Visual Localization are essential components in navigation and mapping for autonomous vehicles especially in GNSS-denied navigation scenarios.

Self-Driving Cars Visual Localization +1

A Unified Framework for Implicit Sinkhorn Differentiation

1 code implementation CVPR 2022 Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Florian Bernard, Daniel Cremers

The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields.

Neural Implicit Representations for Physical Parameter Inference from a Single Video

no code implementations29 Apr 2022 Florian Hofherr, Lukas Koestler, Florian Bernard, Daniel Cremers

Neural networks have recently been used to analyze diverse physical systems and to identify the underlying dynamics.

A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching

1 code implementation CVPR 2022 Paul Roetzer, Paul Swoboda, Daniel Cremers, Florian Bernard

We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes.

Power Bundle Adjustment for Large-Scale 3D Reconstruction

no code implementations27 Apr 2022 Simon Weber, Nikolaus Demmel, Tin Chon Chan, Daniel Cremers

We demonstrate that employing the proposed Power Bundle Adjustment as a sub-problem solver significantly improves speed and accuracy of the distributed optimization.

3D Reconstruction Distributed Optimization

Implicit Shape Completion via Adversarial Shape Priors

no code implementations21 Apr 2022 Abhishek Saroha, Marvin Eisenberger, Tarun Yenamandra, Daniel Cremers

Finally, we show that our adversarial training approach leads to visually plausible reconstructions that are highly consistent in recovering missing parts of a given object.

Point Cloud Completion

The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions

no code implementations CVPR 2022 Dominik Muhle, Lukas Koestler, Nikolaus Demmel, Florian Bernard, Daniel Cremers

However, their approach does not take into account uncertainties, so that the accuracy of the estimated relative pose is highly dependent on accurate feature positions in the target frame.

Lateral Ego-Vehicle Control without Supervision using Point Clouds

no code implementations20 Mar 2022 Florian Müller, Qadeer Khan, Daniel Cremers

In this paper, a framework for training a more robust and scalable model for lateral vehicle control is proposed.

Visual Odometry

Intrinsic Neural Fields: Learning Functions on Manifolds

1 code implementation15 Mar 2022 Lukas Koestler, Daniel Grittner, Michael Moeller, Daniel Cremers, Zorah Lähner

Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative modeling.

Novel View Synthesis

Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applications

no code implementations2 Mar 2022 Qing Cheng, Niclas Zeller, Daniel Cremers

In this paper, we present a complete pipeline for 3D semantic mapping solely based on a stereo camera system.

Autonomous Driving Visual Odometry

DM-VIO: Delayed Marginalization Visual-Inertial Odometry

1 code implementation11 Jan 2022 Lukas von Stumberg, Daniel Cremers

This is the foundation of the proposed pose graph bundle adjustment, which we use for IMU initialization.

Shortest Paths in Graphs with Matrix-Valued Edges: Concepts, Algorithm and Application to 3D Multi-Shape Analysis

1 code implementation8 Dec 2021 Viktoria Ehm, Daniel Cremers, Florian Bernard

Traditionally, the concept of a shortest path is considered for graphs with scalar edge weights, which makes it possible to compute the length of a path by adding up the individual edge weights.

Image Segmentation Semantic Segmentation

Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction

1 code implementation CVPR 2022 Christiane Sommer, Lu Sang, David Schubert, Daniel Cremers

We present Gradient-SDF, a novel representation for 3D geometry that combines the advantages of implict and explicit representations.

3D Reconstruction

Multidirectional Conjugate Gradients for Scalable Bundle Adjustment

no code implementations8 Oct 2021 Simon Weber, Nikolaus Demmel, Daniel Cremers

We revisit the problem of large-scale bundle adjustment and propose a technique called Multidirectional Conjugate Gradients that accelerates the solution of the normal equation by up to 61%.

Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation

no code implementations NeurIPS 2021 Florian Bernard, Daniel Cremers, Johan Thunberg

We address the non-convex optimisation problem of finding a sparse matrix on the Stiefel manifold (matrices with mutually orthogonal columns of unit length) that maximises (or minimises) a quadratic objective function.

Graph Matching

Scalable Sinkhorn Backpropagation

no code implementations29 Sep 2021 Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Florian Bernard, Daniel Cremers

Our main contribution is deriving a simple and efficient algorithm that performs this backward pass in closed form.

Scene Graph Generation for Better Image Captioning?

no code implementations23 Sep 2021 Maximilian Mozes, Martin Schmitt, Vladimir Golkov, Hinrich Schütze, Daniel Cremers

We investigate the incorporation of visual relationships into the task of supervised image caption generation by proposing a model that leverages detected objects and auto-generated visual relationships to describe images in natural language.

Graph Generation Image Captioning +1

Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions

1 code implementation12 Sep 2021 Martin Wudenka, Marcus G. Müller, Nikolaus Demmel, Armin Wedler, Rudolph Triebel, Daniel Cremers, Wolfgang Stürzl

In contrast to most other approaches, our framework can also handle rotation-only motions that are particularly challenging for monocular odometry systems.

Monocular Visual Odometry Optical Flow Estimation

Square Root Marginalization for Sliding-Window Bundle Adjustment

no code implementations ICCV 2021 Nikolaus Demmel, David Schubert, Christiane Sommer, Daniel Cremers, Vladyslav Usenko

The square root formulation pervades three major aspects of our optimization-based sliding-window estimator: for bundle adjustment we eliminate landmark variables with nullspace projection; to store the marginalization prior we employ a matrix square root of the Hessian; and when marginalizing old poses we avoid forming normal equations and update the square root prior directly with a specialized QR decomposition.

Dive into Layers: Neural Network Capacity Bounding using Algebraic Geometry

1 code implementation3 Sep 2021 Ji Yang, Lu Sang, Daniel Cremers

To mathematically prove this, we borrow a tool in topological algebra: Betti numbers to measure the topological geometric complexity of input data and the neural network.

TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset

no code implementations16 Aug 2021 Simon Klenk, Jason Chui, Nikolaus Demmel, Daniel Cremers

The event cameras contain a large sensor of 1280x720 pixels, which is significantly larger than the sensors used in existing stereo event datasets (at least by a factor of ten).

Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification

no code implementations27 Jul 2021 Yu Wang, Yuesong Shen, Daniel Cremers

To learn the direct influence among output nodes in a graph, we propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field.

Node Classification

Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields

no code implementations13 Jul 2021 Hartmut Bauermeister, Emanuel Laude, Thomas Möllenhoff, Michael Moeller, Daniel Cremers

In contrast to existing discretizations which suffer from a grid bias, we show that a piecewise polynomial discretization better preserves the continuous nature of our problem.

Stereo Matching

Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization

no code implementations9 Jul 2021 Jason Chui, Simon Klenk, Daniel Cremers

We propose a novel method for continuous-time feature tracking in event cameras.

DeepLab2: A TensorFlow Library for Deep Labeling

1 code implementation17 Jun 2021 Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, Daniel Cremers, Laura Leal-Taixe, Alan L. Yuille, Florian Schroff, Hartwig Adam, Liang-Chieh Chen

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.

NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

no code implementations CVPR 2021 Marvin Eisenberger, David Novotny, Gael Kerchenbaum, Patrick Labatut, Natalia Neverova, Daniel Cremers, Andrea Vedaldi

We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i. e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them.

Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections

no code implementations31 Mar 2021 Zhenzhang Ye, Tarun Yenamandra, Florian Bernard, Daniel Cremers

While these approaches mainly focus on learning node and edge attributes, they completely ignore the 3D geometry of the underlying 3D objects depicted in the 2D images.

Graph Matching

Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry

no code implementations20 Mar 2021 Qadeer Khan, Patrick Wenzel, Daniel Cremers

Vision-based learning methods for self-driving cars have primarily used supervised approaches that require a large number of labels for training.

Self-Driving Cars Visual Odometry

Square Root Bundle Adjustment for Large-Scale Reconstruction

1 code implementation CVPR 2021 Nikolaus Demmel, Christiane Sommer, Daniel Cremers, Vladyslav Usenko

We propose a new formulation for the bundle adjustment problem which relies on nullspace marginalization of landmark variables by QR decomposition.

Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration

1 code implementation24 Feb 2021 Christian Tomani, Daniel Cremers, Florian Buettner

We address the problem of uncertainty calibration and introduce a novel calibration method, Parametrized Temperature Scaling (PTS).

Variational Data Assimilation with a Learned Inverse Observation Operator

1 code implementation22 Feb 2021 Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer

Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data.

Weather Forecasting

Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry

no code implementations1 Feb 2021 Mariia Gladkova, Rui Wang, Niclas Zeller, Daniel Cremers

To achieve map-based relocalization for direct methods, we integrate image features into Direct Sparse Odometry (DSO) and rely on feature matching to associate online visual odometry (VO) with a previously built map.

Visual Odometry

Post-hoc Uncertainty Calibration for Domain Drift Scenarios

1 code implementation CVPR 2021 Christian Tomani, Sebastian Gruber, Muhammed Ebrar Erdem, Daniel Cremers, Florian Buettner

First, we show that existing post-hoc calibration methods yield highly over-confident predictions under domain shift.

Neural Online Graph Exploration

1 code implementation6 Dec 2020 Ioannis Chiotellis, Daniel Cremers

Can we learn how to explore unknown spaces efficiently?

Future prediction

Isometric Multi-Shape Matching

no code implementations CVPR 2021 Maolin Gao, Zorah Lähner, Johan Thunberg, Daniel Cremers, Florian Bernard

Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer.

3D Reconstruction Object Tracking +1

i3DMM: Deep Implicit 3D Morphable Model of Human Heads

1 code implementation CVPR 2021 Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, Christian Theobalt

Our approach has the following favorable properties: (i) It is the first full head morphable model that includes hair.

Non-Rigid Puzzles

no code implementations26 Nov 2020 Or Litany, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Daniel Cremers

We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation.

SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition

1 code implementation CVPR 2021 Yan Xia, Yusheng Xu, Shuang Li, Rui Wang, Juan Du, Daniel Cremers, Uwe Stilla

We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors.

Metric Learning Point Cloud Retrieval +1

MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

1 code implementation CVPR 2021 Felix Wimbauer, Nan Yang, Lukas von Stumberg, Niclas Zeller, Daniel Cremers

Unlike other multi-view stereo methods, MonoRec is able to reconstruct both static and moving objects by leveraging the predicted masks.

Deep Shells: Unsupervised Shape Correspondence with Optimal Transport

1 code implementation NeurIPS 2020 Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers

We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network.

Unsupervised Dense Shape Correspondence using Heat Kernels

no code implementations23 Oct 2020 Mehmet Aygün, Zorah Lähner, Daniel Cremers

In this work, we propose an unsupervised method for learning dense correspondences between shapes using a recent deep functional map framework.

MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

no code implementations15 Oct 2020 Patrick Dendorfer, Aljoša Ošep, Anton Milan, Konrad Schindler, Daniel Cremers, Ian Reid, Stefan Roth, Laura Leal-Taixé

We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods.

Multiple Object Tracking Multiple People Tracking +2

LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization

no code implementations13 Oct 2020 Lukas von Stumberg, Patrick Wenzel, Nan Yang, Daniel Cremers

The learned features significantly improve the robustness of direct image alignment, especially for relocalization across different conditions.

Pose Estimation

A Chain Graph Interpretation of Real-World Neural Networks

1 code implementation30 Jun 2020 Yuesong Shen, Daniel Cremers

It is thus a promising framework that deepens our understanding of neural networks and provides a coherent theoretical formulation for future deep learning research.

Effective Version Space Reduction for Convolutional Neural Networks

no code implementations22 Jun 2020 Jiayu Liu, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers

In active learning, sampling bias could pose a serious inconsistency problem and hinder the algorithm from finding the optimal hypothesis.

Active Learning Image Classification

PrimiTect: Fast Continuous Hough Voting for Primitive Detection

1 code implementation15 May 2020 Christiane Sommer, Yumin Sun, Erik Bylow, Daniel Cremers

This paper tackles the problem of data abstraction in the context of 3D point sets.

Hamiltonian Dynamics for Real-World Shape Interpolation

1 code implementation ECCV 2020 Marvin Eisenberger, Daniel Cremers

While most prior work focuses on synthetic input shapes, our formulation is designed to be applicable to real-world scans with imperfect input correspondences and various types of noise.

Local Distortion

MOT20: A benchmark for multi object tracking in crowded scenes

1 code implementation19 Mar 2020 Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixé

The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods.

Multi-Object Tracking Multiple Object Tracking with Transformer +1

D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry

no code implementations CVPR 2020 Nan Yang, Lukas von Stumberg, Rui Wang, Daniel Cremers

We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation.

Monocular Depth Estimation Monocular Visual Odometry

Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning

1 code implementation27 Feb 2020 Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers

Structured convex optimization on weighted graphs finds numerous applications in machine learning and computer vision.

Learn to Predict Sets Using Feed-Forward Neural Networks

no code implementations30 Jan 2020 Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Anton Milan, Daniel Cremers, Laura Leal-Taixé, Ian Reid

In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality.

Multi-Label Image Classification object-detection +1

From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds

2 code implementations21 Jan 2020 Christiane Sommer, Yumin Sun, Leonidas Guibas, Daniel Cremers, Tolga Birdal

We propose a new method for segmentation-free joint estimation of orthogonal planes, their intersection lines, relationship graph and corners lying at the intersection of three orthogonal planes.

Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach

1 code implementation13 Dec 2019 Lu Sang, Bjoern Haefner, Daniel Cremers

A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented.

Depth Map Super-Resolution

Informative GANs via Structured Regularization of Optimal Transport

no code implementations4 Dec 2019 Pierre Bréchet, Tao Wu, Thomas Möllenhoff, Daniel Cremers

We tackle the challenge of disentangled representation learning in generative adversarial networks (GANs) from the perspective of regularized optimal transport (OT).

Representation Learning

Efficient Derivative Computation for Cumulative B-Splines on Lie Groups

6 code implementations CVPR 2020 Christiane Sommer, Vladyslav Usenko, David Schubert, Nikolaus Demmel, Daniel Cremers

Continuous-time trajectory representation has recently gained popularity for tasks where the fusion of high-frame-rate sensors and multiple unsynchronized devices is required.

On the well-posedness of uncalibrated photometric stereo under general lighting

no code implementations17 Nov 2019 Mohammed Brahimi, Yvain Quéau, Bjoern Haefner, Daniel Cremers

While the theoretical foundations of this inverse problem under directional lighting are well-established, there is a lack of mathematical evidence for the uniqueness of a solution under general lighting.

Rolling-Shutter Modelling for Direct Visual-Inertial Odometry

no code implementations4 Nov 2019 David Schubert, Nikolaus Demmel, Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers

The visual part of the system performs a photometric bundle adjustment on a sparse set of points.

Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light

no code implementations15 Oct 2019 Eunah Jung, Nan Yang, Daniel Cremers

We propose the concept of a multi-frame GAN (MFGAN) and demonstrate its potential as an image sequence enhancement for stereo visual odometry in low light conditions.

Image Enhancement Optical Flow Estimation +2

Bregman Proximal Framework for Deep Linear Neural Networks

no code implementations8 Oct 2019 Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs

This initiated the development of the Bregman proximal gradient (BPG) algorithm and an inertial variant (momentum based) CoCaIn BPG, which however rely on problem dependent Bregman distances.

Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction

1 code implementation4 Oct 2019 Sebastian Weiss, Robert Maier, Rüdiger Westermann, Daniel Cremers, Nils Thuerey

In a number of tests using synthetic datasets and real-world measurements, we analyse the robustness of our approach and the convergence behavior of the numerical optimization scheme.

Graphics I.6

Towards Generalizing Sensorimotor Control Across Weather Conditions

no code implementations25 Jul 2019 Qadeer Khan, Patrick Wenzel, Daniel Cremers, Laura Leal-Taixé

The ability of deep learning models to generalize well across different scenarios depends primarily on the quality and quantity of annotated data.

Image-to-Image Translation Translation

Smooth Shells: Multi-Scale Shape Registration with Functional Maps

1 code implementation CVPR 2020 Marvin Eisenberger, Zorah Lähner, Daniel Cremers

Smooth shells define a series of coarse-to-fine shape approximations designed to work well with multiscale algorithms.

Flat Metric Minimization with Applications in Generative Modeling

1 code implementation12 May 2019 Thomas Möllenhoff, Daniel Cremers

We take the novel perspective to view data not as a probability distribution but rather as a current.

Learning to Evolve

1 code implementation8 May 2019 Jan Schuchardt, Vladimir Golkov, Daniel Cremers

Here we show that learning to evolve, i. e. learning to mutate and recombine better than at random, improves the result of evolution in terms of fitness increase per generation and even in terms of attainable fitness.

reinforcement-learning Reinforcement Learning (RL)

GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization

1 code implementation26 Apr 2019 Lukas von Stumberg, Patrick Wenzel, Qadeer Khan, Daniel Cremers

Direct SLAM methods have shown exceptional performance on odometry tasks.

Visual-Inertial Mapping with Non-Linear Factor Recovery

7 code implementations13 Apr 2019 Vladyslav Usenko, Nikolaus Demmel, David Schubert, Jörg Stückler, Daniel Cremers

We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO.

Motion Estimation

Variational Uncalibrated Photometric Stereo under General Lighting

1 code implementation ICCV 2019 Bjoern Haefner, Zhenzhang Ye, Maolin Gao, Tao Wu, Yvain Quéau, Daniel Cremers

Photometric stereo (PS) techniques nowadays remain constrained to an ideal laboratory setup where modeling and calibration of lighting is amenable.

Controlling Neural Networks via Energy Dissipation

no code implementations ICCV 2019 Michael Moeller, Thomas Möllenhoff, Daniel Cremers

The last decade has shown a tremendous success in solving various computer vision problems with the help of deep learning techniques.

Computed Tomography (CT) Deblurring +2

Optimization of Inf-Convolution Regularized Nonconvex Composite Problems

no code implementations27 Mar 2019 Emanuel Laude, Tao Wu, Daniel Cremers

In this work, we consider nonconvex composite problems that involve inf-convolution with a Legendre function, which gives rise to an anisotropic generalization of the proximal mapping and Moreau-envelope.

Homogeneous Linear Inequality Constraints for Neural Network Activations

1 code implementation5 Feb 2019 Thomas Frerix, Matthias Nießner, Daniel Cremers

One way to achieve this task is by means of a projection step at test time after unconstrained training.

Photometric Depth Super-Resolution

1 code implementation26 Sep 2018 Bjoern Haefner, Songyou Peng, Alok Verma, Yvain Quéau, Daniel Cremers

This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image.


DeepWrinkles: Accurate and Realistic Clothing Modeling

no code implementations ECCV 2018 Zorah Laehner, Daniel Cremers, Tony Tung

We present a novel method to generate accurate and realistic clothing deformation from real data capture.

Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform

no code implementations6 Aug 2018 Lingni Ma, Jörg Stückler, Tao Wu, Daniel Cremers

Dense pixelwise prediction such as semantic segmentation is an up-to-date challenge for deep convolutional neural networks (CNNs).

Semantic Segmentation

LDSO: Direct Sparse Odometry with Loop Closure

no code implementations3 Aug 2018 Xiang Gao, Rui Wang, Nikolaus Demmel, Daniel Cremers

In this paper we present an extension of Direct Sparse Odometry (DSO) to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO).

Loop Closure Detection Translation

Direct Sparse Odometry with Rolling Shutter

no code implementations ECCV 2018 David Schubert, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers

Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness.

Visual Odometry

The Double Sphere Camera Model

8 code implementations24 Jul 2018 Vladyslav Usenko, Nikolaus Demmel, Daniel Cremers

We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i. e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians.

3D Reconstruction Autonomous Driving +2

Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs

1 code implementation3 Jul 2018 Patrick Wenzel, Qadeer Khan, Daniel Cremers, Laura Leal-Taixé

To this end, we propose to divide the task of vehicle control into two independent modules: a control module which is only trained on one weather condition for which labeled steering data is available, and a perception module which is used as an interface between new weather conditions and the fixed control module.

Self-Driving Cars

Divergence-Free Shape Interpolation and Correspondence

1 code implementation27 Jun 2018 Marvin Eisenberger, Zorah Lähner, Daniel Cremers

We present a novel method to model and calculate deformation fields between shapes embedded in $\mathbb{R}^D$.

q-Space Novelty Detection with Variational Autoencoders

1 code implementation8 Jun 2018 Aleksei Vasilev, Vladimir Golkov, Marc Meissner, Ilona Lipp, Eleonora Sgarlata, Valentina Tomassini, Derek K. Jones, Daniel Cremers

Since abnormal samples are not used during training, we define novelty metrics based on the (partially complementary) assumptions that the VAE is less capable of reconstructing abnormal samples well; that abnormal samples more strongly violate the VAE regularizer; and that abnormal samples differ from normal samples not only in input-feature space, but also in the VAE latent space and VAE output.

Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading

1 code implementation CVPR 2018 Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers

We put forward a principled variational approach for up-sampling a single depth map to the resolution of the companion color image provided by an RGB-D sensor.


Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks

no code implementations ICLR 2019 S. Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Daniel Cremers, Laura Leal-Taixé, Ian Reid

We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.

object-detection Object Detection

The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

4 code implementations17 Apr 2018 David Schubert, Thore Goll, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers

For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements.

Visual Odometry

Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization

no code implementations16 Apr 2018 Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers

We present VI-DSO, a novel approach for visual-inertial odometry, which jointly estimates camera poses and sparse scene geometry by minimizing photometric and IMU measurement errors in a combined energy functional.

Clustering with Deep Learning: Taxonomy and New Methods

2 code implementations23 Jan 2018 Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Maximilian Strobel, Daniel Cremers

In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks.

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

Combinatorial Preconditioners for Proximal Algorithms on Graphs

no code implementations16 Jan 2018 Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers

We present a novel preconditioning technique for proximal optimization methods that relies on graph algorithms to construct effective preconditioners.

BIG-bench Machine Learning

Regularization for Deep Learning: A Taxonomy

no code implementations ICLR 2018 Jan Kukačka, Vladimir Golkov, Daniel Cremers

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other.

Online Photometric Calibration for Auto Exposure Video for Realtime Visual Odometry and SLAM

no code implementations5 Oct 2017 Paul Bergmann, Rui Wang, Daniel Cremers

We further show that our calibration can improve the performance of a state-of-the-art direct visual odometry method that works solely on pixel intensities, calibrating for photometric parameters in an online fashion in realtime.

Camera Calibration Visual Odometry

Variational Reflectance Estimation from Multi-view Images

no code implementations25 Sep 2017 Jean Mélou, Yvain Quéau, Jean-Denis Durou, Fabien Castan, Daniel Cremers

We tackle the problem of reflectance estimation from a set of multi-view images, assuming known geometry.

Video Object Segmentation Without Temporal Information

no code implementations18 Sep 2017 Kevis-Kokitsi Maninis, Sergi Caelles, Yu-Hua Chen, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, Luc van Gool

Video Object Segmentation, and video processing in general, has been historically dominated by methods that rely on the temporal consistency and redundancy in consecutive video frames.

Semantic Segmentation Semi-Supervised Video Object Segmentation +2

Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction

no code implementations12 Sep 2017 Robert Maier, Raphael Schaller, Daniel Cremers

State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors usually reduce drift in camera tracking by globally optimizing the estimated camera poses in real-time without simultaneously updating the reconstructed surface on pose changes.

3D Reconstruction

Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras

no code implementations ICCV 2017 Rui Wang, Martin Schwörer, Daniel Cremers

We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras.

3D Reconstruction Optical Flow Estimation +1

Associative Domain Adaptation

2 code implementations ICCV 2017 Philip Haeusser, Thomas Frerix, Alexander Mordvintsev, Daniel Cremers

Our training scheme follows the paradigm that in order to effectively derive class labels for the target domain, a network should produce statistically domain invariant embeddings, while minimizing the classification error on the labeled source domain.

Association Domain Adaptation +1

Efficient Deformable Shape Correspondence via Kernel Matching

1 code implementation25 Jul 2017 Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers

We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality.

LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution

no code implementations4 Jul 2017 Yvain Quéau, Bastien Durix, Tao Wu, Daniel Cremers, François Lauze, Jean-Denis Durou

The second one directly recovers the depth, by formulating photometric stereo as a system of PDEs which are partially linearized using image ratios.

An Efficient Background Term for 3D Reconstruction and Tracking With Smooth Surface Models

no code implementations CVPR 2017 Mariano Jaimez, Thomas J. Cashman, Andrew Fitzgibbon, Javier Gonzalez-Jimenez, Daniel Cremers

We present a novel strategy to shrink and constrain a 3D model, represented as a smooth spline-like surface, within the visual hull of an object observed from one or multiple views.

3D Reconstruction Object Tracking +1

A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting

no code implementations CVPR 2017 Yvain Queau, Tao Wu, Francois Lauze, Jean-Denis Durou, Daniel Cremers

This paper tackles the photometric stereo problem in the presence of inaccurate lighting, obtained either by calibration or by an uncalibrated photometric stereo method.

KillingFusion: Non-Rigid 3D Reconstruction Without Correspondences

no code implementations CVPR 2017 Miroslava Slavcheva, Maximilian Baust, Daniel Cremers, Slobodan Ilic

We introduce a geometry-driven approach for real-time 3D reconstruction of deforming surfaces from a single RGB-D stream without any templates or shape priors.

3D Reconstruction Unity

Learning by Association -- A Versatile Semi-Supervised Training Method for Neural Networks

no code implementations CVPR 2017 Philip Haeusser, Alexander Mordvintsev, Daniel Cremers

We demonstrate the capabilities of learning by association on several data sets and show that it can improve performance on classification tasks tremendously by making use of additionally available unlabeled data.


Proximal Backpropagation

1 code implementation ICLR 2018 Thomas Frerix, Thomas Möllenhoff, Michael Moeller, Daniel Cremers

Specifically, we show that backpropagation of a prediction error is equivalent to sequential gradient descent steps on a quadratic penalty energy, which comprises the network activations as variables of the optimization.

Learning by Association - A versatile semi-supervised training method for neural networks

1 code implementation3 Jun 2017 Philip Häusser, Alexander Mordvintsev, Daniel Cremers

We demonstrate the capabilities of learning by association on several data sets and show that it can improve performance on classification tasks tremendously by making use of additionally available unlabeled data.


Fusion of Head and Full-Body Detectors for Multi-Object Tracking

no code implementations23 May 2017 Roberto Henschel, Laura Leal-Taixé, Daniel Cremers, Bodo Rosenhahn

In order to track all persons in a scene, the tracking-by-detection paradigm has proven to be a very effective approach.

Multi-Object Tracking

Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect

no code implementations11 May 2017 Nan Yang, Rui Wang, Xiang Gao, Daniel Cremers

Monocular visual odometry (VO) and simultaneous localization and mapping (SLAM) have seen tremendous improvements in accuracy, robustness and efficiency, and have gained increasing popularity over recent years.

Monocular Visual Odometry Simultaneous Localization and Mapping

3D Deep Learning for Biological Function Prediction from Physical Fields

no code implementations13 Apr 2017 Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey Mendenhall, Jens Meiler, Daniel Cremers

In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields.

Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

1 code implementation ICCV 2017 Tim Meinhardt, Michael Moeller, Caner Hazirbas, Daniel Cremers

While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks.

Demosaicking Denoising +1

Deep Depth From Focus

5 code implementations4 Apr 2017 Caner Hazirbas, Sebastian Georg Soyer, Maximilian Christian Staab, Laura Leal-Taixé, Daniel Cremers

Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision.

Dense Multi-view 3D-reconstruction Without Dense Correspondences

no code implementations2 Apr 2017 Yvain Quéau, Jean Mélou, Jean-Denis Durou, Daniel Cremers

We introduce a variational method for multi-view shape-from-shading under natural illumination.

3D Reconstruction

Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras

no code implementations26 Mar 2017 Lingni Ma, Jörg Stückler, Christian Kerl, Daniel Cremers

At test time, the semantics predictions of our network can be fused more consistently in semantic keyframe maps than predictions of a network trained on individual views.

Scene Understanding Semantic Segmentation

Nonlinear Spectral Image Fusion

no code implementations23 Mar 2017 Martin Benning, Michael Möller, Raz Z. Nossek, Martin Burger, Daniel Cremers, Guy Gilboa, Carola-Bibiane Schönlieb

In this paper we demonstrate that the framework of nonlinear spectral decompositions based on total variation (TV) regularization is very well suited for image fusion as well as more general image manipulation tasks.

Image Manipulation

Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer

2 code implementations4 Mar 2017 Vladyslav Usenko, Lukas von Stumberg, Andrej Pangercic, Daniel Cremers

In this paper, we present a real-time approach to local trajectory replanning for microaerial vehicles (MAVs).


Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space

no code implementations CVPR 2017 Matthias Vestner, Roee Litman, Emanuele Rodolà, Alex Bronstein, Daniel Cremers

Many algorithms for the computation of correspondences between deformable shapes rely on some variant of nearest neighbor matching in a descriptor space.

Density Estimation

Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks

no code implementations12 Dec 2016 Sahand Sharifzadeh, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers

We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces.

Autonomous Driving reinforcement-learning +1

Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images

no code implementations NeurIPS 2016 Vladimir Golkov, Marcin J. Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox, Jens Meiler, Daniel Cremers

A contact map is a compact representation of the three-dimensional structure of a protein via the pairwise contacts between the amino acid constituting the protein.

Multiframe Motion Coupling for Video Super Resolution

1 code implementation23 Nov 2016 Jonas Geiping, Hendrik Dirks, Daniel Cremers, Michael Moeller

The idea of video super resolution is to use different view points of a single scene to enhance the overall resolution and quality.

Motion Estimation Video Super-Resolution

Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems

no code implementations ICCV 2017 Thomas Möllenhoff, Daniel Cremers

In this work we show how sublabel-accurate multilabeling approaches can be derived by approximating a classical label-continuous convex relaxation of nonconvex free-discontinuity problems.

One-Shot Video Object Segmentation

4 code implementations CVPR 2017 Sergi Caelles, Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, Luc van Gool

This paper tackles the task of semi-supervised video object segmentation, i. e., the separation of an object from the background in a video, given the mask of the first frame.

Semi-Supervised Video Object Segmentation Video Segmentation +1

Bayesian Inference of Bijective Non-Rigid Shape Correspondence

no code implementations12 Jul 2016 Matthias Vestner, Roee Litman, Alex Bronstein, Emanuele Rodolà, Daniel Cremers

Many algorithms for the computation of correspondences between deformable shapes rely on some variant of nearest neighbor matching in a descriptor space.

Bayesian Inference

A Photometrically Calibrated Benchmark For Monocular Visual Odometry

no code implementations9 Jul 2016 Jakob Engel, Vladyslav Usenko, Daniel Cremers

We present a dataset for evaluating the tracking accuracy of monocular visual odometry and SLAM methods.

Monocular Visual Odometry

Direct Sparse Odometry

2 code implementations9 Jul 2016 Jakob Engel, Vladlen Koltun, Daniel Cremers

We propose a novel direct sparse visual odometry formulation.

Visual Odometry

Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies

1 code implementation7 Apr 2016 Emanuel Laude, Thomas Möllenhoff, Michael Moeller, Jan Lellmann, Daniel Cremers

Convex relaxations of nonconvex multilabel problems have been demonstrated to produce superior (provably optimal or near-optimal) solutions to a variety of classical computer vision problems.

Color Image Denoising Image Denoising +1

Efficient Globally Optimal 2D-to-3D Deformable Shape Matching

no code implementations CVPR 2016 Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt, Michael M. Bronstein, Daniel Cremers

We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the output is a continuous curve on the surface.

3D Shape Retrieval Retrieval

Sublabel-Accurate Relaxation of Nonconvex Energies

2 code implementations CVPR 2016 Thomas Möllenhoff, Emanuel Laude, Michael Moeller, Jan Lellmann, Daniel Cremers

We propose a novel spatially continuous framework for convex relaxations based on functional lifting.

Dense Continuous-Time Tracking and Mapping With Rolling Shutter RGB-D Cameras

no code implementations ICCV 2015 Christian Kerl, Jorg Stuckler, Daniel Cremers

Using a continuous trajectory representation has a number of advantages over a discrete-time representation (e. g. camera poses at the frame interval).

Learning Nonlinear Spectral Filters for Color Image Reconstruction

no code implementations ICCV 2015 Michael Moeller, Julia Diebold, Guy Gilboa, Daniel Cremers

This paper presents the idea of learning optimal filters for color image reconstruction based on a novel concept of nonlinear spectral image decompositions recently proposed by Guy Gilboa.

Image Denoising Image Reconstruction

Entropy Minimization for Convex Relaxation Approaches

no code implementations ICCV 2015 Mohamed Souiai, Martin R. Oswald, Youngwook Kee, Junmo Kim, Marc Pollefeys, Daniel Cremers

Despite their enormous success in solving hard combinatorial problems, convex relaxation approaches often suffer from the fact that the computed solutions are far from binary and that subsequent heuristic binarization may substantially degrade the quality of computed solutions.

Binarization Image Segmentation +1

Model-Based Tracking at 300Hz Using Raw Time-of-Flight Observations

no code implementations ICCV 2015 Jan Stuhmer, Sebastian Nowozin, Andrew Fitzgibbon, Richard Szeliski, Travis Perry, Sunil Acharya, Daniel Cremers, Jamie Shotton

In this paper, we show how to perform model-based object tracking which allows to reconstruct the object's depth at an order of magnitude higher frame-rate through simple modifications to an off-the-shelf depth camera.

Object Tracking

Collaborative Total Variation: A General Framework for Vectorial TV Models

no code implementations6 Aug 2015 Joan Duran, Michael Moeller, Catalina Sbert, Daniel Cremers

Even after over two decades, the total variation (TV) remains one of the most popular regularizations for image processing problems and has sparked a tremendous amount of research, particularly to move from scalar to vector-valued functions.

Deblurring Denoising

Point-wise Map Recovery and Refinement from Functional Correspondence

no code implementations18 Jun 2015 Emanuele Rodolà, Michael Moeller, Daniel Cremers

Since their introduction in the shape analysis community, functional maps have met with considerable success due to their ability to compactly represent dense correspondences between deformable shapes, with applications ranging from shape matching and image segmentation, to exploration of large shape collections.

Image Segmentation Semantic Segmentation

Partial Functional Correspondence

1 code implementation17 Jun 2015 Emanuele Rodolà, Luca Cosmo, Michael M. Bronstein, Andrea Torsello, Daniel Cremers

In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes.

Adopting an Unconstrained Ray Model in Light-Field Cameras for 3D Shape Reconstruction

no code implementations CVPR 2015 Filippo Bergamasco, Andrea Albarelli, Luca Cosmo, Andrea Torsello, Emanuele Rodola, Daniel Cremers

This results in several drawbacks, ranging from the difficulties in feature detection, due to the reduced size of each microlens, to the need to adopt a model with a relatively small number of parameters.

3D Reconstruction 3D Shape Reconstruction

Variational Depth from Focus Reconstruction

1 code implementation1 Aug 2014 Michael Moeller, Martin Benning, Carola Schönlieb, Daniel Cremers

This paper deals with the problem of reconstructing a depth map from a sequence of differently focused images, also known as depth from focus or shape from focus.