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Associations between Aβ deposition and neurometabolic alterations in Alzheimer's disease: Insights from hybrid 3D MRSI-PET imaging

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第6期21卷 e70332页

作者： Miao Zhang Jialin Hu Yaoyu Zhang Ziyu Meng Yibo Zhao Yudu Li Wen Jin Xiao-Hang Qian Lijun Wang Guanyu Ye Xu-Feng Jiang Ya-Dong Li Jian Ye Huidong Tang Jun Liu Parashkev Nachev Zhi-Pei Liang Biao Li Yao Li Department of Nuclear Medicine Ruijin Hospital School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China. National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy Shanghai Jiao Tong University Shanghai China. Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China. Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana Illinois USA. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana Illinois USA. National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana Illinois USA. Department of Geriatrics Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. Medical Center on Aging of Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. Department of Neurology and Institute of Neurology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. Department of Neurovascular Center Changhai Hospital Naval Medical University Shanghai China. Department of Gastroenterology Songjiang Hospital and Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders State Key Laboratory of Eye Health Shanghai Jiao Tong University School of Medicine Shanghai China. Shanghai Key Laboratory of Gynecologic Oncology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China. High-Dimensional Neurology Group Institute of Neurology University College London London UK.

INTRODUCTION:Understanding the neurometabolic changes associated with amyloid-β (Aβ) deposition is important for early Alzheimer's disease (AD) diagnosis, but their spatial relationships remained unexplored due to technical limitations. METHODS:We investigated the relationship between Aβ deposition and neuronal and glial metabolites using high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) (8-min scan, 2 × 3 × 3 mm resolution) and Aβ-positron emission tomography (Aβ-PET) imaging. N-acetylaspartate, myo-inositol, and creatine maps were obtained from 174 participants: 39 controls, 65 mild cognitive impairment (MCI), and 70 AD patients. RESULTS:N-Acetylaspartate levels were negatively correlated with Aβ, while myo-inositol levels were positively correlated globally. Regional associations with Aβ include N-acetylaspartate reductions in frontal cortex, anterior cingulate cortex, and precuneus, and myo-inositol increases in precuneus, lateral temporal, and lateral parietal cortices. Combined MRSI and PET biomarkers achieved the highest diagnostic accuracy for MCI and AD . DISCUSSION:Hybrid high-resolution 3D MRSI and Aβ-PET imaging provides valuable insights into Aβ's impact on neurometabolic changes, improving early AD diagnosis. HIGHLIGHTS:Hybrid 3D magnetic resonance spectroscopic imaging-positron emission tomography (MRSI-PET) imaging reveals Aβ deposition impact on neurometabolism in Alzheimer's disease (AD). N-acetylaspartate (NAA) as a neuronal metabolic marker is negatively associated with Aβ globally and locally. Myo-inositol (mI) as a glial metabolic marker is positively associated with Aβ globally and locally. Combining 3D magnetic resonance spectroscopic imaging (MRSI) and PET biomarkers improves diagnostic accuracy for mild cognitive impairment (MCI) and AD.

关键词： alzheimer's disease amyloid‐β magnetic resonance spectroscopic imaging myo‐inositol n‐acetylaspartate

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Universal joint feature extraction for P300 EEG classification using multi-task autoencoder

arXiv

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arXiv 2018年

作者： Ditthapron, Apiwat Banluesombatkul, Nannapas Ketrat, Sombat Chuangsuwanich, Ekapol Wilaiprasitporn, Theerawit Computer Department Worcester Polytechnic Institute WorcesterMA United States Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Computer Engineering Department Chulalongkorn University Bangkok Thailand

The process of recording Electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this work, we propose the Event-Related Potential Encoder Network (ERPENet);a multi-task autoencoder-based model, that can be applied to any ERP-related tasks. The strength of ERPENet lies in its capability to handle various kinds of ERP datasets and its robustness across multiple recording setups, enabling joint training across datasets. ERPENet incorporates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), in an autoencoder setup, which tries to simultaneously compress the input EEG signal and extract related P300 features into a latent vector. Here, we can infer the process for generating the latent vector as universal joint feature extraction. The network also includes a classification part for attended and unattended events classification as an auxiliary task. We experimented on six different P300 datasets. The results show that the latent vector exhibits better compression capability than the previous state-of-the-art semi-supervised autoencoder model. For attended and unattended events classification, pre-trained weights are adopted as initial weights and tested on unseen P300 datasets to evaluate the adaptability of the model, which shortens the training process as compared to using random Xavier weight initialization. At the compression rate of 6.84, the classification accuracy outperforms conventional P300 classification models: XdawnLDA, DeepConvNet, and EEGNet achieving 79.37% - 88.52% classification accuracy depending on the dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Electroencephalography

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Editor-in-Chief

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IEEE Access 2024年 12卷 i-xi页

作者： Derek Abbott Sonia Aissa Anuradha Annaswamy Nirwan Ansari Jun Cai Kun-Shan Chen Yixin Chen J.-C. Chiao Ayman El-Baz Shaikh Fattah Hoay Gooi Maya Gokhale Chih-Lin Ⅰ Giuseppe Iannaccone Yumi Iwashita Bruce Jacob Abbas Jamalipour Bahram Javidi Weihua Jiang Okyay Kaynak Hulya Kirkici Germano Lambert-Torres Shengtao Li Michal Mrozowski Michele Nappi Dalma Novak Hugo Proença Yi Qian Zhihua Qu Susanto Rahardja Mehrdad Saif Ali Serpengüzel Peter H. Siegel Ashitey Trebi-Ollennu Daniela Tuninetti Ge Wang Keith A. Wear Andreas Weisshaar C. Patrick Yue Enrico Zio School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Institut National de la Recherche Scientifique (INRS) Québec City Canada Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Concordia University Montreal Canada Guilin University of Technology Guilin China Washington University St. Louis MO USA Southern Methodist University Dallas TX USA University of Louisville/Bioengineering Department Louisville KY USA Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Nanyang Technological University Jurong West Singapore Lawrence Livermore National Laboratory Livermore CA USA China Mobile Research Institute Beijing China University of Pisa Pisa Italy NASA-JPL California Institute of Technology Pasadena CA USA Florida Polytechnic University Lakeland FL USA The University of Sydney Sydney NSW Australia University of Connecticut Storrs CT USA Nagaoka University of Technology Nagaoka Niigata Japan Department of Electrical and Electronic Engineering Boğaziçi University Istanbul Turkey University of South Alabama Mobile AL USA Gnarus Institute Itajuba Brazil Xi’an Jiaotong University Xi’an China Gdansk University of Technology Gdansk Poland University of Salerno Fisciano Italy Octane Wireless Hanover MD USA University of Beira Interior Covilhã Portugal Department of Electrical and Computer Engineering University of NebraskaLincoln Lincoln NE USA University of Central Florida Orlando Florida USA Singapore Institute of Technology 10 Dover Dr Singapore Infocomm Technology Cluster 10 Dover Dr Singapore University of Windsor Windsor Canada Department of Physics Koç University Istanbul Turkey California Institute of Technology (CalTech) Pasadena California USA NASA-JPL Mobility and Robotics Systems S

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Wearable Affective Robot

arXiv

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arXiv 2018年

作者： Chen, Min Zhou, Jun Tao, Guangming Yang, Jun Hu, Long Wuhan National Laboratory for Optoelectronics School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Wuhan AIWAC Robotics Co. Ltd China Wuhan National Laboratory for Optoelectronics College of Optoelectronic Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan National Laboratory for Optoelectronics School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology China Wuhan Emotion Smart Sensing Technology Co. Ltd China

With the development of the artificial intelligence (AI), the AI applications have influenced and changed people’s daily life greatly. Here, a wearable affective robot that integrates the affective robot, social robot, brain wearable, and wearable 2.0 is proposed for the first time. The proposed wearable affective robot is intended for a wide population, and we believe that it can improve the human health on the spirit level, meeting the fashion requirements at the same time. In this paper, the architecture and design of an innovative wearable affective robot, which is dubbed as Fitbot, are introduced in terms of hardware and algorithm’s perspectives. In addition, the important functional component of the robot-brain wearable device is introduced from the aspect of the hardware design, EEG data acquisition and analysis, user behavior perception, and algorithm deployment, etc. Then, the EEG based cognition of user’s behavior is realized. Through the continuous acquisition of the in-depth, in-breadth data, the Fitbot we present can gradually enrich user’s life modeling and enable the wearable robot to recognize user’s intention and further understand the behavioral motivation behind the user’s emotion. The learning algorithm for the life modeling embedded in Fitbot can achieve better user’s experience of affective social interaction. Finally, the application service scenarios and some challenging issues of a wearable affective robot are discussed. Copyright © 2018, The Authors. All rights reserved.

关键词： Wearable technology

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Joint learning of discriminative low-dimensional image representations based on dictionary learning and two-layer orthogonal projections

arXiv

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arXiv 2019年

作者： Wei, Xian Shen, Hao Li, Yuanxiang Tang, Xuan Jin, Bo Zhao, Lijun Murphey, Yi Lu Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China Technical University of Munich Germany and fortiss GmbH Munich Germany Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China School of Aeronautics & Astronautics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Department of Electrical and Computer Engineering University of Michigan-Dearborn DearbornMI48128 United States

This work investigates the problem of efficiently learning discriminative low-dimensional representations of multi-class large-scale image objects. We propose a generic deep learning approach by taking advantages of Convolutional Neural Networks (CNN), sparse dictionary learning, and orthogonal projections. CNN is not only powerful on feature extraction, but also robust to spatial variance and changes. Sparse dictionary learning is well known for disentangling nonlinear underlying discriminative factors in data. The orthogonal projection is a notable efficient tool to project multi-class data onto low dimensional discriminative subspace. The proposed procedure can be summarized as follows. At first, a CNN is employed to extract high-dimensional (HD) preliminary convolutional features. Secondly, to avoid the high computational cost of direct sparse coding on HD CNN features, we present to learn sparse representation (SR) in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as a joint optimization problem of trace quotient maximization, which involves the CNN parameters, the orthogonal projection on CNN features, the dictionary and the discriminative projection on sparse codes. The related cost function is well defined on product manifold of the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. It is optimized via a geometrical stochastic gradient descent algorithm. Finally, the quality of dictionary and projections learned by the proposed approach is further investigated in terms of image classification. The experimental results show that the approach can achieve a strongly competitive performance with state-of-the-art image classification methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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IROS 2019 lifelong robotic vision challenge lifelong object recognition report

arXiv

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arXiv 2020年

作者： She, Qi Feng, Fan Liu, Qi Chan, Rosa H.M. Hao, Xinyue Lan, Chuanlin Yang, Qihan Lomonaco, Vincenzo Parisi, German I. Ma, Liang Wu, Jianwen Zhong, Qiaoyong Xie, Di Pu, Shiliang Graffieti, Gabriele Pellegrini, Lorenzo Lomonaco, Vincenzo Maltoni, Davide Wang, Zhengwei Brophy, Eoin Ward, Tomás E. Zhou, Liguang Yang, Lin Goel, Vidit Sheet, Debdoot Kumar, Somesh Song, Soonyong Bae, Heechul Han, Hyonyoung Son, Youngsung Piyasena, Duvindu Kanagarajah, Sathursan Lam, Siew-Kei Wu, Meiqing Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Electronic Engineering Tsinghua University Beijing University of Posts and Telecommunications Beijing China School of Electronic Information Wuhan University Wuhan China ContinualAI Research University of Bologna Italy ContinualAI Research University of Hamburg Germany Intel Labs and ContinualAI Research Hikvision Research Institute Hangzhou China University of Bologna Bologna Italy V-SENSE School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Inisht Centre for Data Analytics School of Computing Dublin City University Dublin Ireland Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Peking University Beijing China Shandong University Qingdao China Beijing Film Academy Beijing China Indian Institute of Technology Kharagpur India Korea Republic of Nanyang Technological University Singapore Singapore

This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top 8 finalists (out of over 150 teams). The competition dataset (L)ifel(O)ng (R)obotic V(IS)ion (OpenLORIS) - Object Recognition (OpenLORIS-object) is designed for driving lifelong/continual learning research and application in robotic vision domain, with everyday objects in home, office, campus, and mall scenarios. The dataset explicitly quantifies the variants of illumination, object occlusion, object size, camera-object distance/angles, and clutter information. Rules are designed to quantify the learning capability of the robotic vision system when faced with the objects appearing in the dynamic environments in the contest. Individual reports, dataset information, rules, and released source code can be found at the project homepage. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Visualization and Classification of Diseases using Deep Learning Based Convolution Neural Network

引用

IOP Conference Series: Materials science and Engineering 2020年第1期937卷

作者： D.A Janeera P. Amudhavalli P Sherubha S.P Sasirekha P. Anantha Christu Raj R Sitharthan Assistant Professor Sri Krishna College of Engineering and Technology Coimbatore Assistant Professor Department of Computer Applications B S AbdurRahman Crescent Institute of Science and Technology Chennai Independent Researcher Coimbatore ORCID: 0000-0002-5945-2402 Research Scholar Karpagam Academy of Higher Education Coimbatore Assistant Professor Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore School of Electrical Engineering Vellore Institute of Technology Vellore

In the contemporary phase of big data, data visualization is one of the challenging segment of the discovery process. For a classifier, the primary goal is to identify the hidden levels of big data. The performance of classifiers depends on the feature space, number of classes and size of the data. To improve the reliability, efficiency and accuracy of the classifiers, new algorithms are required for analysis. This paper enables classification and visualization of information on diseases using a Deep Learning based Convolution Neural Network classifier. For feature selection and handling massive data in analysis of multivariate data is performed using particle swarm optimization (PSO) and principal component analysis (PCA) techniques. Real-world datasets are utilized for demonstration of the proposed learning algorithm. Deep learning classifiers are scientifically higher and offers better performance when compared to other classifiers according to the comparative study.

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A holistic visual place recognition approach using lightweight CNNs for significant viewpoint and appearance changes

arXiv

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arXiv 2018年

作者： Khaliq, Ahmad Ehsan, Shoaib Chen, Zetao Milford, Michael McDonald-Maier, Klaus Embedded and Intelligent System Lab School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Vision for Robotics Lab Eth Zurich Zurich8092 Switzerland Australian Centre for Robotic Vision School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Robot applications

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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Purely Geometric scene association and retrieval - A case for macro-scale 3D geometry

arXiv

引用

arXiv 2018年

作者： Sawhney, Rahul Li, Fuxin Christensen, Henrik I. Isbell, Charles L. Institute of Robotics & Intelligent Machines College of Computing Georgia Institute of Technology Atlanta United States School of Electrical Engineering and Computer Science Oregon State University Corvallis United States Institute of Contextual Robotics University of California San Diego United States

We address the problems of measuring geometric similarity between 3D scenes, represented through point clouds or range data frames, and associating them. Our approach leverages macro-scale 3D structural geometry - the relative configuration of arbitrary surfaces and relationships among structures that are potentially far apart. We express such discriminative information in a viewpoint-invariant feature space. These are subsequently encoded in a frame-level signature that can be utilized to measure geometric similarity. Such a characterization is robust to noise, incomplete and partially overlapping data besides viewpoint changes. We show how it can be employed to select a diverse set of data frames which have structurally similar content, and how to validate whether views with similar geometric content are from the same scene. The problem is formulated as one of general purpose retrieval from an unannotated, spatio-temporally unordered database. Empirical analysis indicates that the presented approach thoroughly outperforms baselines on depth / range data. Its depth-only performance is competitive with state-of-the-art approaches with RGB or RGB-D inputs, including ones based on deep learning. Experiments show retrieval performance to hold up well with much sparser databases, which is indicative of the approach's robustness. The approach generalized well - it did not require dataset specific training, and scaled up in our experiments. Finally, we also demonstrate how geometrically diverse selection of views can result in richer 3D reconstructions. Copyright © 2018, The Authors. All rights reserved.

关键词： geometry

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