Search Results for author:
Found 23 papers, 16 papers with code
AffineGlue: Joint Matching and Robust Estimation
Moreover, we derive a new minimal solver for homography estimation, requiring only a single affine correspondence (AC) and a gravity prior.
A Large Scale Homography Benchmark
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.
A Large-Scale Homography Benchmark
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.
Matching with AffNet based rectifications
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
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
(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
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)
Efficient Initial Pose-graph Generation for Global SfM
We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms.
Differentiable Data Augmentation with Kornia
In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors.
ArXiving Before Submission Helps Everyone
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
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
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.
Benchmarking Classic and Learned Navigation in Complex 3D Environments
However, this new line of work is largely disconnected from well-established classic navigation approaches.
Leveraging Outdoor Webcams for Local Descriptor Learning
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
The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images.
Ranked #3 on
Deblurring
on REDS
Repeatability Is Not Enough: Learning Affine Regions via Discriminability
A method for learning local affine-covariant regions is presented.
Ranked #4 on
Image Matching
on IMC PhotoTourism
(using extra training data)
Working hard to know your neighbor's margins: Local descriptor learning loss
We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT.
In the Saddle: Chasing Fast and Repeatable Features
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
The paper systematically studies the impact of a range of recent advances in CNN architectures and learning methods on the object categorization (ILSVRC) problem.
All you need is a good init
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)).
Ranked #23 on
Image Classification
on MNIST
WxBS: Wide Baseline Stereo Generalizations
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
A novel algorithm for wide-baseline matching called MODS - Matching On Demand with view Synthesis - is presented.
Two-View Matching with View Synthesis Revisited
Wide-baseline matching focussing on problems with extreme viewpoint change is considered.
