Search Results for author: Dmytro Mishkin

Found 23 papers, 16 papers with code

AffineGlue: Joint Matching and Robust Estimation

no code implementations28 Jul 2023 Daniel Barath, Dmytro Mishkin, Luca Cavalli, Paul-Edouard Sarlin, Petr Hruby, Marc Pollefeys

Moreover, we derive a new minimal solver for homography estimation, requiring only a single affine correspondence (AC) and a gravity prior.

Homography Estimation

A Large Scale Homography Benchmark

2 code implementations20 Feb 2023 Daniel Barath, Dmytro Mishkin, Michal Polic, Wolfgang Förstner, Jiri Matas

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 benchmark leveraging Pi3D.

Homography Estimation Surface Normal Estimation

A Large-Scale Homography Benchmark

no code implementations CVPR 2023 Daniel Barath, Dmytro Mishkin, Michal Polic, Wolfgang Förstner, Jiri Matas

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 benchmark leveraging Pi3D.

Homography Estimation Surface Normal Estimation

Matching with AffNet based rectifications

no code implementations29 Jul 2022 Václav Vávra, Dmytro Mishkin, Jiří Matas

We consider the problem of two-view matching under significant viewpoint changes with view synthesis.

OpenGlue: Open Source Graph Neural Net Based Pipeline for Image Matching

1 code implementation19 Apr 2022 Ostap Viniavskyi, Mariia Dobko, Dmytro Mishkin, Oles Dobosevych

We present OpenGlue: a free open-source framework for image matching, that uses a Graph Neural Network-based matcher inspired by SuperGlue \cite{sarlin20superglue}.

Learning and Crafting for the Wide Multiple Baseline Stereo

no code implementations22 Dec 2021 Dmytro Mishkin

(ii) The descriptor trained with the HardNeg loss, called HardNet, is compact and shows state-of-the-art performance in standard matching, patch verification and retrieval benchmarks.


HarrisZ$^+$: Harris Corner Selection for Next-Gen Image Matching Pipelines

no code implementations27 Sep 2021 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 descriptors and to more robust matching strategies, also thanks to the advent of the deep learning and the increased computational power of the modern hardware.

 Ranked #1 on Image Matching on IMC PhotoTourism (using extra training data)

Image Matching

Differentiable Data Augmentation with Kornia

1 code implementation19 Nov 2020 Jian Shi, Edgar Riba, Dmytro Mishkin, Francesc Moreno, Anguelos Nicolaou

In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors.

Image Augmentation Image Manipulation +1

ArXiving Before Submission Helps Everyone

no code implementations11 Oct 2020 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 scientific community.

Image Matching across Wide Baselines: From Paper to Practice

5 code implementations3 Mar 2020 Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, Eduard Trulls

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 our primary metric.


Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

4 code implementations5 Oct 2019 Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski

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.

Camera Calibration Edge Detection +10

Benchmarking Classic and Learned Navigation in Complex 3D Environments

1 code implementation30 Jan 2019 Dmytro Mishkin, Alexey Dosovitskiy, Vladlen Koltun

However, this new line of work is largely disconnected from well-established classic navigation approaches.


Leveraging Outdoor Webcams for Local Descriptor Learning

1 code implementation28 Jan 2019 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.


DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

12 code implementations CVPR 2018 Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, Jiri Matas

The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images.

Deblurring object-detection +1

In the Saddle: Chasing Fast and Repeatable Features

1 code implementation24 Aug 2016 Javier Aldana-Iuit, Dmytro Mishkin, Ondrej Chum, Jiri Matas

A novel similarity-covariant feature detector that extracts points whose neighbourhoods, when treated as a 3D intensity surface, have a saddle-like intensity profile.

Systematic evaluation of CNN advances on the ImageNet

1 code implementation7 Jun 2016 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.

Object Categorization

All you need is a good init

7 code implementations ICLR 2015 Dmytro Mishkin, Jiri Matas

Experiment with different activation functions (maxout, ReLU-family, tanh) show that the proposed initialization leads to learning of very deep nets that (i) produces networks with test accuracy better or equal to standard methods and (ii) is at least as fast as the complex schemes proposed specifically for very deep nets such as FitNets (Romero et al. (2015)) and Highway (Srivastava et al. (2015)).

Image Classification

WxBS: Wide Baseline Stereo Generalizations

2 code implementations24 Apr 2015 Dmytro Mishkin, Jiri Matas, Michal Perdoch, Karel Lenc

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 factor such as viewpoint, illumination, sensor type or where object appearance changes significantly, e. g. over time.

MODS: Fast and Robust Method for Two-View Matching

2 code implementations9 Mar 2015 Dmytro Mishkin, Jiri Matas, Michal Perdoch

A novel algorithm for wide-baseline matching called MODS - Matching On Demand with view Synthesis - is presented.

Vocal Bursts Valence Prediction

Two-View Matching with View Synthesis Revisited

no code implementations17 Jun 2013 Dmytro Mishkin, Michal Perdoch, Jiri Matas

Wide-baseline matching focussing on problems with extreme viewpoint change is considered.

Vocal Bursts Valence Prediction

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