Computer Vision and Learning Lab

analyzing_inverse_problems

Code for the paper "Analyzing inverse problems with invertible neural networks." (2018)

conditional_INNs

Code for the paper "Guided Image Generation with Conditional Invertible Neural Networks" (2019)

ControlNet-XS

DSACLine

Differentiable RANSAC: Learning Robust Line Fitting

dsacstar

DSAC* for Visual Camera Re-Localization (RGB or RGB-D)

esac

ESAC - Expert Sample Consensus Applied To Camera Re-Localization

FrEIA

Framework for Easily Invertible Architectures

LessMore

Learning Less is More - 6D Camera Localization via 3D Surface Regression

ngdsac_camreloc

Neural-Guided, Differentiable RANSAC for Camera Re-Localization (NG-DSAC++)

ngransac

Neural-Guided RANSAC for Estimating Epipolar Geometry from Sparse Correspondences

vislearn/FrEIA

Framework for Easily Invertible Architectures

vislearn/ControlNet-XS

vislearn/ngransac

Neural-Guided RANSAC for Estimating Epipolar Geometry from Sparse Correspondences

vislearn/LessMore

Learning Less is More - 6D Camera Localization via 3D Surface Regression

vislearn/dsacstar

DSAC* for Visual Camera Re-Localization (RGB or RGB-D)

vislearn/DSACLine

Differentiable RANSAC: Learning Robust Line Fitting

vislearn/conditional_INNs

Code for the paper "Guided Image Generation with Conditional Invertible Neural Networks" (2019)

vislearn/analyzing_inverse_problems

Code for the paper "Analyzing inverse problems with invertible neural networks." (2018)

vislearn/esac

ESAC - Expert Sample Consensus Applied To Camera Re-Localization

vislearn/ngdsac_camreloc

Neural-Guided, Differentiable RANSAC for Camera Re-Localization (NG-DSAC++)

vislearn/IB-INN

Code for the paper "Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification" (2020)

vislearn/FFF

Free-form flows are a generative model training a pair of neural networks via maximum likelihood

vislearn/ngdsac_horizon

Neural-Guided, Differentiable RANSAC for Horizon Line Estimation

vislearn/GIN

Code for the paper "Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (2020)

vislearn/HINT

Code for the research paper "HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference".

vislearn/trustworthy_GCs

Materials for the paper https://arxiv.org/pdf/2007.15036.pdf

vislearn/3dcv-students

Exercises for the lecture "Computer Vision: 3D Reconstruction"

vislearn/inn_toy_data

Code for artificial toy data sets used to evaluate (conditional) invertible neural networks and related methods

vislearn/Coupling-Universality

vislearn/gmbench

Code for “A Comparative Study of Graph Matching Algorithms in Computer Vison” (ECCV 2022)

vislearn/ML-Tutorial

Introduction to Machine Learning using scikit-learn and PyTorch

vislearn/MLAE

Maximum Likelihood Training of Autoencoders

vislearn/libct

A fast approximate primal-dual solver for tracking-by-assignment cell-tracking problems

vislearn/combilp

Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation

vislearn/libmpopt

Library for Message Passing Optimization Techniques

vislearn/Gaussianization-Bound

Code to paper "On the Convergence Rate of Gaussianization with Random Rotations"

vislearn/Coupling-Flow-Bound

vislearn/FrEIA-community

Community-driven effort to improve and extend FrEIA

vislearn/tkr-graphmatching

Dual decomposition based graph matching solver by Torresani, Kolmogorov and Rother