322 citations · 1.4k across the 18 of their papers we have counts for
24 papers
DF26: We Cannot Tell Fake From Real Anymore
Severyn Shykula, Andrii Yermakov, Ivan Samarskyi +3
We introduce DF26, a novel benchmark for detecting AI-generated videos containing fully synthetic clips produced by recent text-to-video and image-to-video models. The videos captu…
Explaining Human Preferences via Metrics for Structured 3D Reconstruction
Jack Langerman, Denys Rozumnyi, Yuzhong Huang +1
"What cannot be measured cannot be improved" while likely never uttered by Lord Kelvin, summarizes effectively the driving force behind this work. This paper presents a detailed di…
AffineGlue: Joint Matching and Robust Estimation
Daniel Barath, Dmytro Mishkin, Luca Cavalli +3
We propose AffineGlue, a method for joint two-view feature matching and robust estimation that reduces the combinatorial complexity of the problem by employing single-point minimal…
A Large Scale Homography Benchmark
Daniel Barath, Dmytro Mishkin, Michal Polic +2
We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchm…
Matching with AffNet based rectifications
Václav Vávra, Dmytro Mishkin, Jiří Matas
We consider the problem of two-view matching under significant viewpoint changes with view synthesis. We propose two novel methods, minimizing the view synthesis overhead. The firs…
OpenGlue: Open Source Graph Neural Net Based Pipeline for Image Matching
Ostap Viniavskyi, Mariia Dobko, Dmytro Mishkin +1
We present OpenGlue: a free open-source framework for image matching, that uses a Graph Neural Network-based matcher inspired by SuperGlue \cite{sarlin20superglue}. We show that in…
Learning and Crafting for the Wide Multiple Baseline Stereo
Dmytro Mishkin
This thesis introduces the wide multiple baseline stereo (WxBS) problem. WxBS, a generalization of the standard wide baseline stereo problem, considers the matching of images that…
HarrisZ: Harris Corner Selection for Next-Gen Image Matching Pipelines
Fabio Bellavia, Dmytro Mishkin
Due to its role in many computer vision tasks, image matching has been subjected to an active investigation by researchers, which has lead to better and more discriminant feature d…
Efficient Initial Pose-graph Generation for Global SfM
Daniel Barath, Dmytro Mishkin, Ivan Eichhardt +2
We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms. To avoid forming tentative point correspondences by FLANN and geometric v…
Differentiable Data Augmentation with Kornia
Jian Shi, Edgar Riba, Dmytro Mishkin +2
In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors. This module leverages different…
ArXiving Before Submission Helps Everyone
Dmytro Mishkin, Amy Tabb, Jiri Matas
We claim, and present evidence, that allowing arXiv publication before a conference or journal submission benefits researchers, especially early career, as well as the whole scient…
Image Matching across Wide Baselines: From Paper to Practice
Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk +4
We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as o…
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
Edgar Riba, Dmytro Mishkin, Daniel Ponsa +2
This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems. The pa…
Benchmarking Classic and Learned Navigation in Complex 3D Environments
Dmytro Mishkin, Alexey Dosovitskiy, Vladlen Koltun
Navigation research is attracting renewed interest with the advent of learning-based methods. However, this new line of work is largely disconnected from well-established classic n…
Leveraging Outdoor Webcams for Local Descriptor Learning
Milan Pultar, Dmytro Mishkin, Jiří Matas
We present AMOS Patches, a large set of image cut-outs, intended primarily for the robustification of trainable local feature descriptors to illumination and appearance changes. Im…
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych +2
We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art perf…
Repeatability Is Not Enough: Learning Affine Regions via Discriminability
Dmytro Mishkin, Filip Radenovic, Jiri Matas
A method for learning local affine-covariant regions is presented. We show that maximizing geometric repeatability does not lead to local regions, a.k.a features,that are reliably…
Working hard to know your neighbor's margins: Local descriptor learning loss
Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic +1
We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT. We show that the proposed loss that maximizes the dist…
In the Saddle: Chasing Fast and Repeatable Features
Javier Aldana-Iuit, Dmytro Mishkin, Ondrej Chum +1
A novel similarity-covariant feature detector that extracts points whose neighbourhoods, when treated as a 3D intensity surface, have a saddle-like intensity profile. The saddle co…
Systematic evaluation of CNN advances on the ImageNet
Dmytro Mishkin, Nikolay Sergievskiy, Jiri Matas
The paper systematically studies the impact of a range of recent advances in CNN architectures and learning methods on the object categorization (ILSVRC) problem. The evalution tes…
All you need is a good init
Dmytro Mishkin, Jiri Matas
Layer-sequential unit-variance (LSUV) initialization - a simple method for weight initialization for deep net learning - is proposed. The method consists of the two steps. First, p…
WxBS: Wide Baseline Stereo Generalizations
Dmytro Mishkin, Jiri Matas, Michal Perdoch +1
We have presented a new problem -- the wide multiple baseline stereo (WxBS) -- which considers matching of images that simultaneously differ in more than one image acquisition fact…
MODS: Fast and Robust Method for Two-View Matching
Dmytro Mishkin, Jiri Matas, Michal Perdoch
A novel algorithm for wide-baseline matching called MODS - Matching On Demand with view Synthesis - is presented. The MODS algorithm is experimentally shown to solve a broader rang…
Two-View Matching with View Synthesis Revisited
Dmytro Mishkin, Michal Perdoch, Jiri Matas
Wide-baseline matching focussing on problems with extreme viewpoint change is considered. We introduce the use of view synthesis with affine-covariant detectors to solve such probl…