171 citations · 535 across the 42 of their papers we have counts for
12 papers · 1 filter
Universe Points Representation Learning for Partial Multi-Graph Matching
Zhakshylyk Nurlanov, Frank R. Schmidt, Florian Bernard
Many challenges from natural world can be formulated as a graph matching problem. Previous deep learning-based methods mainly consider a full two-graph matching setting. In this wo…
Conjugate Product Graphs for Globally Optimal 2D-3D Shape Matching
Paul Roetzer, Zorah Lähner, Florian Bernard
We consider the problem of finding a continuous and non-rigid matching between a 2D contour and a 3D mesh. While such problems can be solved to global optimality by finding a short…
HandFlow: Quantifying View-Dependent 3D Ambiguity in Two-Hand Reconstruction with Normalizing Flow
Jiayi Wang, Diogo Luvizon, Franziska Mueller +4
Reconstructing two-hand interactions from a single image is a challenging problem due to ambiguities that stem from projective geometry and heavy occlusions. Existing methods are d…
A Comparative Study of Graph Matching Algorithms in Computer Vision
Stefan Haller, Lorenz Feineis, Lisa Hutschenreiter +5
The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide ra…
Unsupervised Deep Multi-Shape Matching
Dongliang Cao, Florian Bernard
3D shape matching is a long-standing problem in computer vision and computer graphics. While deep neural networks were shown to lead to state-of-the-art results in shape matching,…
Efficient and Flexible Sublabel-Accurate Energy Minimization
Zhakshylyk Nurlanov, Daniel Cremers, Florian Bernard
We address the problem of minimizing a class of energy functions consisting of data and smoothness terms that commonly occur in machine learning, computer vision, and pattern recog…
A Unified Framework for Implicit Sinkhorn Differentiation
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +2
The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields. One major reason is its ease of integration into deep learning framework…
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching
Paul Roetzer, Paul Swoboda, Daniel Cremers +1
We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant forma…
The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions
Dominik Muhle, Lukas Koestler, Nikolaus Demmel +2
The estimation of the relative pose of two camera views is a fundamental problem in computer vision. Kneip et al. proposed to solve this problem by introducing the normal epipolar…
Neural Implicit Representations for Physical Parameter Inference from a Single Video
Florian Hofherr, Lukas Koestler, Florian Bernard +1
Neural networks have recently been used to analyze diverse physical systems and to identify the underlying dynamics. While existing methods achieve impressive results, they are lim…
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions
Tarun Yenamandra, Ayush Tewari, Nan Yang +3
Neural 3D implicit representations learn priors that are useful for diverse applications, such as single- or multiple-view 3D reconstruction. A major downside of existing approache…
Structured Prediction Problem Archive
Paul Swoboda, Bjoern Andres, Andrea Hornakova +6
Structured prediction problems are one of the fundamental tools in machine learning. In order to facilitate algorithm development for their numerical solution, we collect in one pl…