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Memory efficient large-scale image-based localization

MULTIMEDIA TOOLS AND APPLICATIONS 2015年第2期74卷 479-503页

作者： Lu, Guoyu Sebe, Nicu Xu, Congfu Kambhamettu, Chandra Univ Delaware Video Image Modeling & Synth Lab Newark DE 19711 USA Univ Trento Dept Informat Engn & Comp Sci I-38100 Trento Italy Zhejiang Univ Inst Artificial Intelligence Hangzhou 310027 Zhejiang Peoples R China

Local features have been widely used in the area of image-based localization. However, large-scale 2D-to-3D matching problems still involve massive memory consumption, which is mainly caused by the high dimensionality of the features (e.g. 128 dimensions of SIFT feature). This paper introduces a new method that decreases local features' high dimensionality for reducing memory capacity and accelerating the descriptor matching process. With this new method, all descriptors are projected into a lower dimensional space through the new learned matrices that are able to reduce the curse of dimensionality in the large scale image-based localization. The low dimensional descriptors are then mapped into a Hamming space for further reducing the memory requirement. This study also proposes an image-based localization pipeline based on the new learned Hamming descriptors. The new learned descriptor and the localization pipeline are applied to two challenging datasets. The experimental results show that the proposed method achieves extraordinary image registration performance compared with the published results from state-of-the-art methods.

关键词： image-based localization Large scale imagery SIFT Hamming descriptor Dimensionality reduction

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3D POINT CLOUD SIMPLIFICATION FOR image-based localization

3D POINT CLOUD SIMPLIFICATION FOR IMAGE-BASED LOCALIZATION

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IEEE International Conference on Multimedia & Expo Workshops (ICMEW)

作者： Cheng, Wentao Lin, Weisi Sun, Ming-Ting Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Univ Washington Dept Elect Engn Seattle WA 98195 USA

ISBN: (纸本)9781479970797

Recent advances in image-based localization have made it possible to estimate the camera pose relative to 3D point clouds. In a multimedia cloud computing paradigm, large-scale 3D point clouds can be stored on the server side, and this enables us to implement image-based localization applications on mobile devices toward many cloud-media applications and services. When scaling the 3D point cloud database up to a world-wide level, the prohibitive memory footprint of such large-scale point clouds is a heavy burden to the server side. In this paper, we present a method to compute a compact subset of a 3D point cloud for image-based localization while achieving acceptable localization performance. In our method, we consider both coverage of database images and emphasis on 3D points of high spatial significance, and we show that our method outperforms the state-of-the-art methods on three public datasets especially when the size of the resultant subset is small. In essence, the priority of system resource is given to points of high spatial significance.

关键词： image-based localization point cloud simplification set cover multimedia cloud

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image-based localization Using Prior Map Database and Monte Carlo localization 11

Image-based Localization Using Prior Map Database and Monte ...

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11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Kim, Hyongjin Oh, Taekjun Lee, Donghwa Myung, Hyun Korea Adv Inst Sci & Technol Dept Civil & Environm Engn Daejeon 305701 South Korea

ISBN: (纸本)9781479953332

The aim of this paper is to propose an image and map data-based localization method applicable to a variety of environments. For the localization, we use prior map database, image-based localization method, and MCL (Monte Carlo localization). The results were confirmed by open data set in a variety of environments. The experimental results show the feasibility of the proposed method for the robot localization.

关键词： image-based localization image Feature Prior Map Database MCL

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Discriminative Feature-to-Point Matching in image-based localization 27

Discriminative Feature-to-Point Matching in Image-Based Loca...

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27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Donoser, Michael Schmalstieg, Dieter Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria

ISBN: (纸本)9781479951178

The prevalent approach to image-based localization is matching interest points detected in the query image to a sparse 3D point cloud representing the known world. The obtained correspondences are then used to recover a precise camera pose. The state-of-the-art in this field often ignores the availability of a set of 2D descriptors per 3D point, for example by representing each 3D point by only its centroid. In this paper we demonstrate that these sets contain useful information that can be exploited by formulating matching as a discriminative classification problem. Since memory demands and computational complexity are crucial in such a setup, we base our algorithm on the efficient and effective random fern principle. We propose an extension which projects features to fern-specific embedding spaces, which yields improved matching rates in short runtime. Experiments first show that our novel formulation provides improved matching performance in comparison to the standard nearest neighbor approach and that we outperform related randomization methods in our localization scenario.

关键词： Camera Pose Estimation Classification image-based localization

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An Efficient Indoor localization based on Deep Attention Learning Model

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Computer Systems Science & Engineering 2023年第8期46卷 2637-2650页

作者： Amr Abozeid Ahmed I.Taloba Rasha M.Abd El-Aziz Alhanoof Faiz Alwaghid Mostafa Salem Ahmed Elhadad Department of Computer Science College of Science and ArtsJouf UniversityQurayyatSaudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityEgypt Computer Science Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

Indoor localization methods can help many sectors,such as healthcare centers,smart homes,museums,warehouses,and retail malls,improve their service *** a result,it is crucial to look for low-cost methods that can provide exact localization in indoor *** this context,imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an ***-based localization faces many issues,such as image scale and rotation ***,image-based localization’s accuracy and speed(latency)are two critical *** paper proposes an efficient 6-DoF deep-learning model for image-based *** model incorporates the channel attention module and the Scale PyramidModule(SPM).It not only enhances accuracy but also ensures the model’s real-time *** complex scenes,a channel attention module is employed to distinguish between the textures of the foregrounds and *** model adapted an SPM,a feature pyramid module for dealing with image scale and rotation variance ***,the proposed model employs two regressions(two fully connected layers),one for position and the other for orientation,which increases outcome *** on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error(MSE)for both position and *** the indoor 7-Scenes dataset,the MSE for the position is reduced to 0.19 m and 6.25°for the ***,on the outdoor Cambridge landmarks dataset,the MSE for the position is reduced to 0.63 m and 2.03°for the *** to the findings,the proposed approach is superior and more successful than the baseline methods.

关键词： image-based localization computer vision deep learning attention module VGG-16

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Street-Level image localization based on Building-Aware Features via Patch-Region Retrieval under Metropolitan-Scale

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REMOTE SENSING 2021年第23期13卷 4876-4876页

作者： Zhi, Lanyue Xiao, Zhifeng Qiang, Yonggang Qian, Linjun Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Remote Sensing 129 Luoyu Rd Wuhan 430079 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China

The aim of image-based localization (IBL) is to localize the real location of query image by matching reference image in database with GNSS-tags. Popular methods related to IBL commonly use street-level images, which have high value in practical application. Using street-level image to tackle IBL task has the primary challenges: existing works have not made targeted optimization for urban IBL tasks. Besides, the matching result is over-reliant on the quality of image features. Methods should address their practicality and robustness in engineering application, under metropolitan-scale. In response to these, this paper made following contributions: firstly, given the critical of buildings in distinguishing urban scenes, we contribute a feature called Building-Aware Feature (BAF). Secondly, in view of negative influence of complex urban scenes in retrieval process, we propose a retrieval method called Patch-Region Retrieval (PRR). To prove the effectiveness of BAF and PRR, we established an image-based localization experimental framework. Experiments prove that BAF can retain the feature points that fall on the building, and selectively lessen the feature points that fall on other things. While this effectively compresses the storage amount of feature index, we can also improve recall of localization results;implemented in the stage of geometric verification, PRR compares matching results of regional features and selects the best ranking as final result. PRR can enhance effectiveness of patch-regional feature. In addition, we fully confirmed the superiority of our proposed methods through a metropolitan-scale street-level image dataset.

关键词： image-based localization street-level image building-aware feature patch-region retrieval metropolitan-scale

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localization of the Chang'e-5 Lander Using Radio-Tracking and image-based Methods

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REMOTE SENSING 2021年第4期13卷 590-590页

作者： Wang, Jia Zhang, Yu Di, Kaichang Chen, Ming Duan, Jianfeng Kong, Jing Xie, Jianfeng Liu, Zhaoqin Wan, Wenhui Rong, Zhifei Liu, Bin Peng, Man Wang, Yexin Beijing Aerosp Control Ctr BACC Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China CAS Ctr Excellence Comparat Planetol Hefei 230026 Peoples R China

Chang'e-5, China's first unmanned lunar sample-return mission, was successfully landed in Northern Oceanus Procellarum on 1 December 2020. Determining the lander location precisely and timely is critical for both engineering operations and subsequent scientific research. localization of the lander was performed using radio-tracking and image-based methods. The lander location was determined to be (51.92 degrees W, 43.06 degrees N) by both methods. Other localization results were compared for cross-validation. The localization results greatly contributed to the planning of the ascender lifting off from the lander and subsequent maneuvers, and they will contribute to scientific analysis of the returned samples and in situ acquired data.

关键词： Chang’ e-5 lander localization radio-tracking descent images image-based localization

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PDC-Net plus : Enhanced Probabilistic Dense Correspondence Network

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第8期45卷 10247-10266页

作者： Truong, Prune Danelljan, Martin Timofte, Radu Van Gool, Luc Comp Vis Lab ETH Zurich CH-8092 Zurich Switzerland Univ Wurzburg Germany D-97074 Wurzburg Germany Katholieke Univ Leuven B-3000 Leuven Belgium

Establishing robust and accurate correspondences between a pair of images is a long-standing computer vision problem with numerous applications. While classically dominated by sparse methods, emerging dense approaches offer a compelling alternative paradigm that avoids the keypoint detection step. However, dense flow estimation is often inaccurate in the case of large displacements, occlusions, or homogeneous regions. In order to apply dense methods to real-world applications, such as pose estimation, image manipulation, or 3D reconstruction, it is therefore crucial to estimate the confidence of the predicted matches. We propose the Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences along with a reliable confidence map. We develop a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty. In particular, we parametrize the predictive distribution as a constrained mixture model, ensuring better modelling of both accurate flow predictions and outliers. Moreover, we develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction in the context of self-supervised training. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets. We further validate the usefulness of our probabilistic confidence estimation for the tasks of pose estimation, 3D reconstruction, image-based localization, and image retrieval.

关键词： Correspondence estimation dense flow regression probabilistic flow uncertainty estimation geometric matching optical flow pose estimation image-based localization 3D reconstruction

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ventura, Jonathan Kukelova, Zuzana Sattler, Torsten Barath, Daniel Cal Poly Dept Comp Sci & Software Engn San Luis Obispo CA USA Czech Tech Univ Visual Recognit Grp Fac Elect Engn Prague Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Swiss Fed Inst Technol Dept Comp Sci Comp Vision & Geometry Grp Zurich Switzerland

ISBN: (纸本)9798350353006

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https: //***/danini/absolute-pose-from-orientedand-scaled-features.

关键词： absolute pose computer vision geometric vision image-based localization minimal solver

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BoQ: A Place is Worth a Bag of Learnable Queries

BoQ: A Place is Worth a Bag of Learnable Queries

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ali-Bey, Amar Chaib-Draa, Brahim Giguere, Philippe Univ Laval Dept Comp Sci & Software Engn Quebec City PQ Canada

ISBN: (纸本)9798350353006

In visual place recognition, accurately identifying and matching images of locations under varying environmental conditions and viewpoints remains a significant challenge. In this paper, we introduce a new technique, called Bag-of-Queries (BoQ), which learns a set of global queries, designed to capture universal place-specific attributes. Unlike existing techniques that employ self-attention and generate the queries directly from the input, BoQ employ distinct learnable global queries, which probe the input features via cross-attention, ensuring consistent information aggregation. In addition, this technique provides an interpretable attention mechanism and integrates with both CNN and Vision Transformer backbones. The performance of BoQ is demonstrated through extensive experiments on 14 large-scale benchmarks. It consistently outperforms current state-of-the-art techniques including NetVLAD, MixVPR and EigenPlaces. Moreover, despite being a global retrieval technique (one-stage), BoQ surpasses two-stage retrieval methods, such as Patch-NetVLAD, TransVPR and R2Former, all while being orders of magnitude faster and more efficient. The code and model weights are publicly available at https://***/amaralibey/Bag-of-Queries.

关键词： image Geo-localization image Retrieval image-based localization NetVLAD Place Recognition Visual localization Visual Place Recognition VPR

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