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Are Large-Scale 3D Models Really Necessary for Accurate Visual localization?

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第3期43卷 814-829页

作者： Torii, Akihiko Taira, Hajime Sivic, Josef Pollefeys, Marc Okutomi, Masatoshi Pajdla, Tomas Sattler, Torsten Tokyo Inst Technol Sch Engn Dept Syst & Control Engn Tokyo 1528550 Japan PSL Res Univ Ecole Normale Super Dept Informat INRIAWILLOW ProjectENSCNRSUMR 8548 F-75006 Paris France Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague 16000 Czech Republic Swiss Fed Inst Technol Dept Comp Sciene CH-8092 Zurich Switzerland Microsoft CH-8304 Wallisellen Switzerland Chalmers Univ Technol S-41296 Gothenburg Sweden

Accurate visual localization is a key technology for autonomous navigation. 3D structure-based methods employ 3D models of the scene to estimate the full 6 degree-of-freedom (DOF) pose of a camera very accurately. However, constructing (and extending) large-scale 3D models is still a significant challenge. In contrast, 2D image retrieval-based methods only require a database of geo-tagged images, which is trivial to construct and to maintain. They are often considered inaccurate since they only approximate the positions of the cameras. Yet, the exact camera pose can theoretically be recovered when enough relevant database images are retrieved. In this paper, we demonstrate experimentally that large-scale 3D models are not strictly necessary for accurate visual localization. We create reference poses for a large and challenging urban dataset. Using these poses, we show that combining image-based methods with local reconstructions results in a higher pose accuracy compared to state-of-the-art structure-based methods, albeight at higher run-time costs. We show that some of these run-time costs can be alleviated by exploiting known database image poses. Our results suggest that we might want to reconsider the need for large-scale 3D models in favor of more local models, but also that further research is necessary to accelerate the local reconstruction process.

关键词： Three-dimensional displays Solid modeling Visualization Cameras Two dimensional displays Databases Pose estimation Visual localization image-based localization place recognition pose estimation image retrieval

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A frame-based probabilistic local verification method for robust correspondence

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2022年第0期192卷 232-243页

作者： Shen, Liang Xu, Zhou Zhu, Jiahua Huang, Xiaotao Jin, Tian Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410000 Peoples R China Natl Univ Def Technol Coll Meteorol & Oceanog Changsha 410000 Peoples R China

Establishing reliable feature correspondence between two sets of features is a fundamental task in image processing. In this paper, we propose a novel probabilistic local verification method to reject false feature matches. We exploit the local affine frame to calculate the re-projection error, and develop a novel probabilistic model to estimate the correspondence confidence according to the error. The correspondence confidence is evaluated by calculating the posterior probability based on a two-layer mixture model. The key parameters of the proposed method can be adaptively estimated by alternatively maximizing and updating a second lower bound function. We also suggest that the adjacent inlier neighbors are good neighbors and thereby proposing a confidence-distance-ratio strategy to balance the inlier confidence and spatial distance. Our method mostly outperforms other state-of-the-art methods by over ten percentage points in the success rate of UAV localization tasks, and by over six percentage points in the F-measure on multiple public test datasets.

关键词： Locality Preservation Matching Robust feature correspondence Outlier rejection Mismatch removal UAV localization image-based localization Feature matching

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View Synthesis with Scene Recognition for Cross-View image localization

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FUTURE INTERNET 2023年第4期15卷 126-126页

作者： Lee, Uddom Jiang, Peng Wu, Hongyi Xin, Chunsheng Old Dominion Univ Sch Cybersecur Norfolk VA 23529 USA Old Dominion Univ Dept ECE Norfolk VA 23529 USA Univ Arizona Dept ECE Tucson AZ 85721 USA

image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes. Our design features a unique scheme to ensure that the synthesized images contain the important features from both reference and patch images, covering seasonable features and minimizing the gap for the image-based localization tasks. The performance evaluation shows that the proposed framework can synthesize the views in various weather and lighting conditions.

关键词： image-based localization deep learning generative neural networks style transfer

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Preserving Privacy in Mobile Spatial Computing 32

Preserving Privacy in Mobile Spatial Computing

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32nd Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV) Part of MMSys Conference

作者： Wu, Nan Cheng, Ruizhi Chen, Songqing Han, Bo George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9781450393836

Mapping and localization are the key components in mobile spatial computing to facilitate interactions between users and the digital model of the physical world. To enable localization, mobile devices keep capturing images of the real-world surroundings and uploading them to a server with spatial maps for localization. This leads to privacy concerns on the potential leakage of sensitive information in both spatial maps and localization images (e.g., when used in confidential industrial settings or our homes). Motivated by the above issues, we present a holistic research agenda in this paper for designing principled approaches to preserve privacy in spatial mapping and localization. We introduce our ongoing research, including learning-assisted noise generation to shield spatial maps, distributed architecture with intelligent aggregation to protect localization images, and end-to-end privacy preservation with fully homomorphic encryption. We also discuss the technical challenges, our preliminary results, and open research problems in those areas.

关键词： Mobile Spatial Computing 3D Spatial Maps image-based localization Privacy Leakage Structure from Motion Fully Homomorphic Encryption

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Registration of Consecutive Frames From Wireless Capsule Endoscopy for 3D Motion Estimation

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IEEE ACCESS 2021年 9卷 119533-119545页

作者： Oliveira, Marina Araujo, Helder Figueiredo, Isabel N. Pinto, Luis Curto, Eva Perdigoto, Luis Univ Coimbra Fac Sci & Technol Dept Elect & Comp Engn DEEC P-3004531 Coimbra Portugal Univ Coimbra Inst Syst & Robot P-3004531 Coimbra Portugal Univ Coimbra Fac Sci & Technol Dept Math CMUC P-3004531 Coimbra Portugal Polytech Inst Leiria ESTG P-2411901 Leiria Portugal

Wireless Capsule Endoscopy (WCE) is a non-invasive medical procedure devised for painless in vivo inspection of the gastrointestinal (GI) tract. It is especially valuable for the examination of the small intestine since it is difficult to reach by traditional endoscopic procedures. The setup includes a camera with an embedded light source and a circuit capable of acquiring and transmitting the video. The main challenge of this technology is the identification of the position and trajectory of the capsule as it travels through the GI tract, which is particularly relevant during the detection of anomalies in the tissue. Given only the information provided by the recorded images, it is possible to estimate the 3D motion of the camera capsule and provide a full trajectory reconstruction. A critical yet difficult step in this process is the image registration between sequential frames. Therefore, being able to determine accurate correspondences between points, regions or features in two consecutive frames is crucial for the computation of the relative rotation and translation of the capsule. This paper comprises a comparative assessment of methodologies to address this problem with a porcine colon dataset obtained with our experimental setup.

关键词： Cameras Feature extraction Location awareness Three-dimensional displays Colon Trajectory Estimation Capsule movement deep leaning feature extraction image-based localization optical flow wireless capsule endoscopy

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Egocentric visitor localization and artwork detection in cultural sites using synthetic data

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PATTERN RECOGNITION LETTERS 2020年 133卷 17-24页

作者： Orlando, Santi Andrea Furnari, Antonino Farinella, Giovanni Maria Univ Catania Dept Math & Comp Sci Via Santa Sofia 64 I-95125 Catania Italy DWORD Xenia Srl Catania Italy ICAR CNR Natl Res Council Palermo Italy

Computer vision and machine learning can be used in cultural heritage to augment the experience of visitors during the exploration of the cultural site, as well as to assist its management. To achieve such goals, two fundamental tasks should be addressed, i.e., localizing visitors and recognizing the observed artworks. Wearable cameras offer a convenient setting to address both tasks through the analysis of images acquired from the visitors' points of view. However, the engineering of approaches to address such tasks generally requires large amounts of labeled data. We propose a tool which can be used to collect and automatically label synthetic visual data suitable to study image-based localization and artwork detection. The tool simulates a virtual agent navigating the 3D model of a real cultural site and automatically captures video frames along with the related ground truth camera poses and semantic masks indicating the position of artworks. We generate a dataset of synthetic images starting from the 3D model of a museum located in Siracusa, Italy. The experiments suggest that the proposed tool allows to drastically reduce the effort needed to collect and label data, providing a means to generate large-scale datasets suitable to study localization and artwork detection in cultural sites. (C) 2020 Elsevier B.V. All rights reserved.

关键词： image-based localization Artwork detection Similarity search Simulated data

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Deep 6-DoF camera relocalization in variable and dynamic scenes by multitask learning

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MACHINE VISION AND APPLICATIONS 2023年第3期34卷 37-37页

作者： Wang, Junyi Qi, Yue Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Beihang Univ Qingdao Res Inst Qingdao Peoples R China

Recently, direct visual localization with convolutional neural networks has attracted researchers' attention with achieving an end-to-end process. However, on the one side, the lack of using 3D information leads to imprecise accuracy. Meanwhile, the single input image confuses the relocalization in the scenes that keep similar views at different positions. On the other side, the relocalization problem in variable or dynamic scenes is still challenging. Concentrating on these concerns, we propose two multitask relocalization networks called MMLNet and MMLNet+ for obtaining the 6-DoF camera pose in static, variable and dynamic scenes. Firstly, addressing the dataset lack of variable scenes, we construct a variable scene dataset with a semiautomatic process combining SFM and MVS algorithms with a few manual labels. based on the process, three scenes covering an office, a bedroom and a sitting room are gathered and generated. Secondly, to enhance the perception between 2D images and 3D poses, we design a multitask network called MMLNet that regresses both camera pose and scene point cloud. Meanwhile, the Chamfer distance is joined into the original pose loss to optimize MMLNet. Moreover, MMLNet learns the pose trajectory feature by using LSTM layers to the additional pose array input, which meanwhile breaks through the limitation of single image input. based on the MMLNet, aiming at dynamic and variable scenes, MMLNet+ outputs the auxiliary segmentation branch that distinguishes fixed, changeable or dynamic parts of the input image. Furthermore, we define the feature fusion block to implement the feature sharing among three tasks, further promoting the performance in dynamic and variable environments. Finally, experiments on static, dynamic and our constructed variable datasets demonstrate state-of-the-art relocalization performances of MMLNet and MMLNet+. Simultaneously, the positive effects of the pose learning part, reconstruction branch and segmentation task are als

关键词： image-based localization Deep learning Dynamic localization Multitask learning

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A Comparison of Visual Navigation Approaches based on localization and Reinforcement Learning in Virtual and Real Environments 15

A Comparison of Visual Navigation Approaches based on Locali...

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15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 15th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Rosano, Marco Furnari, Antonino Gulino, Luigi Farinella, Giovanni Maria Univ Catania Catania Italy OrangeDev Srl Via Vasco de Gama 91 Florence Italy

ISBN: (纸本)9789897584022

Visual navigation algorithms allow a mobile agent to sense the environment and autonomously find its way to reach a target (e.g. an object in the environment). While many recent approaches tackled this task using reinforcement learning, which neglects any prior knowledge about the environments, more classic approaches strongly rely on self-localization and path planning. In this study, we compare the performance of single-target and multi-target visual navigation approaches based on the reinforcement learning paradigm, and simple baselines which rely on image-based localization. Experiments performed on discrete-state environments of different sizes, comprised of both real and virtual images, show that the two paradigms tend to achieve complementary results, hence suggesting that a combination of the two approaches to visual navigation may be beneficial.

关键词： Visual Navigation Reinforcement Learning image-based localization First Person Vision

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PanoraMIS: An ultra-wide field of view image dataset for vision-based robot-motion estimation

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2020年第9期39卷 1037-1051页

作者： Benseddik, Houssem-Eddine Morbidi, Fabio Caron, Guillaume Univ Picardie Jules Verne MIS Lab Amiens France

This article presents a new dataset of ultra-wide field of view images with accurate ground truth, calledPanoraMIS. The dataset covers a large spectrum of panoramic cameras (catadioptric, twin-fisheye), robotic platforms (wheeled, aerial, and industrial robots), and testing environments (indoors and outdoors), and it is well suited to rigorously validate novel image-based robot-motion estimation algorithms, including visual odometry, visual SLAM, and deep learning-based methods. PanoraMIS and the accompanying documentation is publicly available on the Internet for the entire research community.

关键词： Panoramic cameras omnidirectional vision visual-inertial odometry image-based localization autonomous robots

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A SOCP-based Automatic Visual Fingerprinting Method for Indoor localization System

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IEEE ACCESS 2019年 7卷 72862-72871页

作者： Yin, Xiliang Ma, Lin Tan, Xuezhi Qin, Danyang Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China Harbin Vocat & Tech Coll Dept Elect & Informat Engn Harbin 150081 Heilongjiang Peoples R China Heilongjiang Univ Elect Engn Coll Harbin 150080 Heilongjiang Peoples R China

With the development of fingerprinting-based visual localization technology, a problem with this method is standing out, which is it takes great expenses in fingerprint collection. Recently, a few studies proposed some methods to alleviate this problem. However, the accuracy of the existing method is relatively low under some scenarios such as wide field of vision. In this paper, we propose a novel automatic visual fingerprinting (AVF) method for an indoor visual localization system. We consider the performance of AVF greatly hinges on visual odometry (VO) and ego-motion estimation (EME) block, which are two different ways of estimating fingerprint coordinates. Since both visual odometry and ego-motion estimation model are inaccurate, we build the least square model by second-order cone programming (SOCP). Our SOCP-based method is proposed to deal with the serious cumulative error and the random error introduced by VO and EME model, respectively. The purpose of this paper is improving the accuracy of the database generated by the AVF method under wide field of vision scenarios. Although the time costs are relatively higher than our compared method, fortunately, it is only on the off-line stage. The simulation results show that our method can provide a reliable image-location database with the consumer-grade smartphone camera.

关键词： SOCP AVF motion model image-based localization indoor localization

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