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computer-vision-10

Camera Localization

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in camera-localization-10

Trend

Dataset

Best Model

Actions

Oxford RobotCar Full

Aachen Day-Night benchmark

Libraries

Use these libraries to find camera-localization-10 models and implementations

Datasets

Aachen Day-Night

12 Scenes

HPS

CrossLoc Benchmark Datasets

Subtasks

Camera Relocalization

Most implemented papers

Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses

Eric Brachmann, C. Rother•Thu May 09 2019

This work uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets and combines neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible.

270

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Introduction Benchmarks Datasets Subtasks Libraries Papers

Paper Graph

Event-based Camera Pose Tracking using a Generative Event Model

D. Scaramuzza, Guillermo Gallego, Elias Mueggler, Christian Forster•Tue Oct 06 2015

This work tackles the problem of event-based camera localization in a known environment, without additional sensing, using a probabilistic generative event model in a Bayesian filtering framework and proposes to use the contrast residual as a measure of how well the estimated pose of the event- based camera and the environment explain the observed events.

62 0

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Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Kaiwei Wang, Kailun Yang, Dongming Sun, Ruiqi Cheng, Shufei Lin, Weijian Hu, Xiao Huang, Huabing Li, J. Bai•Mon May 13 2019

Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper and the experiments carried on the public datasets and in the field illustrate the validation of the proposed system.

26 0

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CMRNet: Camera to LiDAR-Map Registration

Wolfram Burgard, A. Ballardini, Daniele Cattaneo, Matteo Vaghi, S. Fontana, D. Sorrenti•Sun Jun 23 2019

This paper presents CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data, which is the first CNN-based approach that learns to match images from a monocular camera to a given, preexisting 3D LiDar-map.

76 0

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R2D2: Reliable and Repeatable Detector and Descriptor

Philippe Weinzaepfel, Jérôme Revaud, M. Humenberger, César Roberto de Souza•Mon Dec 31 2018

This work argues that repeatable regions are not necessarily discriminative and can therefore lead to select suboptimal keypoints, and proposes to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.

420 0

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6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference

Shadi Albarqouni, L. Guibas, N. Navab, Slobodan Ilic, Tolga Birdal, Haowen Deng, Mai Bui•Wed Apr 08 2020

A multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses, using Bingham distributions and a multivariate Gaussian to model the position, with an end-to-end deep neural network.

31 0

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Back to the Feature: Learning Robust Camera Localization from Pixels to Pose

V. Lepetit, M. Pollefeys, Torsten Sattler, Carl Toft, Lars Hammarstrand, Fredrik Kahl, Maans Larsson, Paul-Edouard Sarlin, Viktor Larsson, Ajaykumar Unagar, Hugo Germain•Mon Mar 15 2021

PixLoc is introduced, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model, based on the direct alignment of multiscale deep features, casting camera localization as metric learning.

294 0

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Mapping and Localization from Planar Markers

M. Marín-Jiménez, R. Muñoz-Salinas, E. Yeguas-Bolivar, R. Carnicer•Tue May 31 2016

This paper proposes a novel method to simultaneously solve the problems of mapping and localization from a set of squared planar markers and shows that the method performs better than Structure from Motion and visual SLAM techniques.

141 0

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Camera Localization | State-of-the-Art