Journal ArticleDOI
City-Scale Localization for Cameras with Known Vertical Direction
Reads0
Chats0
TLDR
This work considers the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known, and extends accurate approximations and fast polynomial solvers to camera pose estimation.Abstract:
We consider the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known. In principle, this is just an instance of camera pose estimation, but the scale of the problem introduces some interesting challenges. Most importantly, it makes the correspondence problem very difficult so there will often be a significant number of outliers to handle. To tackle this problem, we use recent theoretical as well as technical advances. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental results are given demonstrating that it is possible to reliably estimate the camera pose despite cases with more than 99 percent outlier correspondences in city-scale models with several millions of 3D points.read more
Citations
More filters
Proceedings ArticleDOI
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Torsten Sattler,Will Maddern,Carl Toft,Akihiko Torii,Lars Hammarstrand,Erik Stenborg,Daniel Safari,Daniel Safari,Masatoshi Okutomi,Marc Pollefeys,Marc Pollefeys,Josef Sivic,Fredrik Kahl,Fredrik Kahl,Tomas Pajdla +14 more
TL;DR: This paper introduces the first benchmark datasets specifically designed for analyzing the impact of day-night changes, weather and seasonal variations, as well as sequence-based localization approaches and the need for better local features on visual localization.
Proceedings ArticleDOI
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
Mihai Dusmanu,Ignacio Rocco,Tomas Pajdla,Marc Pollefeys,Josef Sivic,Akihiko Torii,Torsten Sattler +6 more
TL;DR: This work proposes an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector, and shows that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations.
Proceedings ArticleDOI
From Coarse to Fine: Robust Hierarchical Localization at Large Scale
TL;DR: HF-Net is proposed, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization and sets a new state-of-the-art on two challenging benchmarks for large-scale localization.
Proceedings ArticleDOI
Image-Based Localization Using LSTMs for Structured Feature Correlation
Florian Walch,Caner Hazirbas,Laura Leal-Taixé,Torsten Sattler,Sebastian Hilsenbeck,Daniel Cremers +5 more
TL;DR: Experimental results show the proposed CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes outperforms existing deep architectures, and can localize images in hard conditions, where classic SIFT-based methods fail.
Posted Content
R2D2: Repeatable and Reliable Detector and Descriptor.
Jerome Revaud,Philippe Weinzaepfel,César Roberto de Souza,Noe Pion,Gabriela Csurka,Yohann Cabon,Martin Humenberger +6 more
TL;DR: This work argues that salient regions are not necessarily discriminative, and therefore can harm the performance of the description, and proposes to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.
References
More filters
Book
Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Multiple View Geometry in Computer Vision.
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
Proceedings Article
Fast approximate nearest neighbors with automatic algorithm configuration
Marius Muja,David G. Lowe +1 more
TL;DR: A system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data?” and a new algorithm that applies priority search on hierarchical k-means trees, which is found to provide the best known performance on many datasets.
Book
Using Algebraic Geometry
TL;DR: The Berlekamp-Massey-Sakata Decoding Algorithm is used for solving Polynomial Equations and for computations in Local Rings.
Proceedings ArticleDOI
IM2GPS: estimating geographic information from a single image
James Hays,Alexei A. Efros +1 more
TL;DR: This paper proposes a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach and shows that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.