• Ronald Clark

    I am a research fellow at Imperial College London where I work on 3D vision and machine learning and was recently fortunate enough to receive an Imperial College Early Career Fellowship. I did my PhD at the University of Oxford Department of Computer Science where I was funded by an 中国怎么上youtub scholarship. Before coming to the UK I did my BSc and MSc in electrical engineering at the instagram - 软件下载中心 手机软件下载 绿色软件 免费电脑 ...:2021-12-4 · instagram众一种快速、美妙和有趣的方式将你在任何地方随时抓拍下的图片分享彼此,而且它允许用户选择图片的滤镜样式,并且拥有多种美化工具,如果你是一个照片爱好者,那么千万不要错过这款软件 … in South Africa.

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    Updates
    • Jun, 2024: CVPR'20 Outstanding Reviewer Award
    • Sep, 2024: 1 paper accepted at NeurIPS'19 as a spotlight!
    • Jul, 2024: 2 papers accepted at ICCV'19.
    • Jul, 2024: Invited Talk at the BMVA technical meeting on Geometry and Deep Learning.
    • March, 2024: Our 2nd Workshop on Deep Learning for Visual SLAM will be held at ICCV'19!
    • Jan, 2024: Honoured to be an early-career ICRF recipient.
    • Dec, 2018: Invited Talk at Microsoft Research Cambridge.
    • Dec, 2018: Co-organizing the CVPR'19 Workshop on Deep Learning for Visual Semantic Navigation
    • Jun, 2018: CVPR'18 Best Paper Honourable Mention Award

    Publications


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    Scalable Uncertainty for Computer Vision with Functional Variational Inference
    Eduardo D C Carvalho, Ronald Clark, Andrea Nicastro, Paul H J Kelly
    Conference on Computer Vision and Pattern Recognition (免费翻国外墙的app), 2024
    pdf / bibtex

    We use approximate Bayesian inference in deep models to obtain uncertainty estimates with the same scalability as deteministic models.

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    Anomaly Detection for Time Series Using a VAE-LSTM Hybrid Model
    Shuyu Lin, 中国怎么上ins, Robert Birke, Sandro Schonborn, Niki Trigoni, Stephen Roberts
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024
    pdf / code

    An unsupervised spatio-temporal approach for anomaly detection.

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    Towards the Probabilistic Fusion of Learned Priors into Standard Pipelines for 3D Reconstruction
    Tristan Laidlow, Jan Czarnowski, Andrea Nicastro, Ronald Clark, Stefan Leutenegger
    IEEE International Conference on Robotics and Automation (ICRA), 2024
    pdf / 苹果怎么翻ins墙

    An optimisation based approach for integrating learned priors into 3D reconstruction pipelines.

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    DeepFactors: Real-Time Probabilistic Dense Monocular SLAM
    Jan Czarnowski, Tristan Laidlow, Ronald Clark, Andrew J. Davison
    IEEE Robotics and Automation Letters (RAL), 2024
    pdf / bibtex

    We introduce deep factor graphs for dense visual SLAM.

    Learning Meshes for Dense Visual SLAM
    Michael Bloesch, Tristan Laidlow, Ronald Clark, Stefan Leutenegger, Andrew J. Davison
    Instagram在国内使用教程在国内怎么使用ins_文档下载:MAC OS X 无法上 instagram 刷新 INS 动态怎么处 理目前国内限制了登陆上 g...今天我伔说说怎么在电脑上国外网站,需要用到的是:外游加速器 下面上教程: 1.... IOS版Instagram图文视频打不开解决方法 IOS版Instagram图文视频打不开解决方法_计算机硬件及 (ICCV), 2024
    arxiv / bibtex

    Meshes for multiview reconstruction.

    X-Section: Cross-section Prediction for Enhanced RGBD Fusion
    Andrea Nicastro, Ronald Clark, Stefan Leutenegger
    IEEE International Conference on Computer Vision (ICCV), 2024 (Oral)
    arxiv / facebook

    We propose a model to predict the thickness of objects and use it to improve the accuracy of multi-view RGB-D reconstruction.

    Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
    Bo Yang, Jianan Wang, 中国怎么上ins, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni
    加速兔_百度百科:2021-8-24 · 加速兔是一款高速的网络加速软件。采用目前先进和流行的互联网技术,拥有全新自主研发的高速通信协议;中国唯一一家启用服务器中转加速技术,ip识别功能技术的高速网络加速软件。加速兔网络加速器诞生于2021年,是“深圳加速兔网络科技有限公司”推出的专门针对国内访问国外日益恶劣的 ... (NeurIPS), 2024 (Spotlight)
    arxiv / bibtex

    A simple and accurate method for 3D instance segmentation on point clouds.

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    fēnix 3 HR | 停产&停止维修产品 | 产品资讯 | Garmin | 中国 ...:2021-1-9 · 软件更新完成后,建议您先到户外空旷处收星,可加快卫星定位。 注意事項: 此软件只适用中国大陆地区销售的设备。 当您在执行软件更新程序前,请先将设备内的数据备份,众免在更新时遗失您保存在GPS设备上的数据。 更新前请先移除其他USB存储设备。
    Shuyu Lin, Stephen Roberts, Niki Trigoni, Ronald Clark
    arxiv preprint
    arxiv

    A principled approach for balancing the reconstruction-regularisation tradeoff in VAE models.

    Learning Semantically Meaningful Embeddings Using Linear Constraints
    Shuyu Lin, Bo Yang, Robert Birke, Ronald Clark
    International Conference on Computer Vision and Pattern Recognition (CVPR-W) Workshops, 2024
    arxiv / bibtex

    We investigate the use of invertible decoders for learning a semantically meaningful latent space.

    WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding
    Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen Roberts
    International Conference on Learning Representations (ICLR) Workshops, 2024
    arxiv / bibtex

    The reconstruction quality of VAEs can be significantly improved by using aggregate posteriors.

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    Fusion++: Volumetric Object-Level SLAM
    John McCormac*, Ronald Clark*, Michael Bloesch, Stefan Leutenegger, Andrew J. Davison
    International Conference on 3D Vision (3DV'18), 2018
    bibtex

    By combining visual SLAM and deep learning we can create object-level maps of the world.

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    Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison
    European Conference on Computer Vision (ECCV'18), 2018
    project page / video / bibtex

    We can increase the convergence rate of least-squares optimisers by learning the update steps.

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    InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
    Wenbin Li, Sajad Saeedi, John McCormac, Ronald Clark, Dimos Tzoumanikas, Qing Ye, Yuzhong Huang, Rui Tang, Stefan Leutenegger
    British Machine Vision Conference (BMVC), 2018
    project page / video / bibtex

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    CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM   (Best Paper Honorable Mention)
    Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, Andrew J. Davison
    International Conference on Computer Vision (CVPR), 2018
    project page/ 中国怎么上twitter / bibtex

    Scenes can be represented by the latent space of an autoencoder and optimized for multi-view consistency.

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    Meta Learning for Instance-Level Data Association
    Ronald Clark, John McCormac, Stefan Leutenegger, Andrew J. Davison
    Neural Information Processing Systems (NIPS'17) Workshop on MetaLearning, 2018
    bibtex

    Learning the finetuning process can improve the performance of video object segmentation.

    Efficient Indoor Positioning with Visual Experiences via Lifelong Learning
    Hongkai Wen, Ronald Clark, Sen Wang, Xiaoxuan Lu, Bowen Du, Wen Hu, Niki Trigoni
    IEEE Transactions on Mobile Computing (TMC), 2018
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    Visual localisation can be made more efficient by using predictive, second-order information.

    3D Object Reconstruction from a Single Depth View with Adversarial Learning
    Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni
    IEEE International Conference on Computer Vision Workshops (ICCV-W), 2017
    code (official) / bibtex

    We can use a GAN to learn the conditional distribution of 3D objects given a depth map.

    A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
    Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni
    Computer Vision and Pattern Recognition (CVPR), 2017
    code (third-party) / bibtex

    A model for visual localization that exploits temporal smoothness.

    VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
    中国怎么上instagram, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni
    AAAI Conference on Artificial Intelligence (AAAI) , 2017
    project page / code (third-party) / bibtex

    Recurrent Neural Networks can be used to fuse visual and inertial information to estimate the motion of a device.

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    Sen Wang, Ronald Clark, Hongkai Wen, Niki Trigoni
    International Journal of Robotics Research (IJRR), 2017
    project page / code (third-party) / bibtex

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    Sen Wang, Ronald Clark, Hongkai Wen, Niki Trigoni
    bibtex

    Conference version of above paper.

    手机vnp的服务器怎么填

    Large Scale Indoor Keyframe-based Localization using Geomagnetic Field and Motion Pattern
    Sen Wang, Hongkai Wen, Ronald Clark, Andrew Markham, Niki Trigoni
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016
    bibtex

    Graph-based SLAM using a magnetometer.

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    免费翻国外墙的app, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni
    IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2016
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    We can improve the efficiency of visual localization by using an informed matching.

    Optimization of a Hybrid Energy System through Fast Convex Programming
    Ronald Clark, W.A. Cronje, Anton van Wyk
    IEEE International Conference on Intelligent Systems Modelling and Simulation (ISMS), 2014
    bibtex

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    System for the recognition of online handwritten mathematical expressions
    Ronald Clark, Quik Kung, Anton van Wyk
    IEEE International Conference on Computer as a Tool (EUROCON), 2014
    bibtex

    A system for recognising and solving handwritten equations.

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    Recognising Handwritten Mathematical expressions using an Ensemble of SVM Classifiers
    Ronald Clark, Quik Kung, Anton van Wyk
    13th Symposium of the Pattern Recognition Association of South Africa (PRASA/IAPR), 2012
    bibtex

    Thesis

    Visual-inertial odometry, mapping and re-localization through learning.
    Ronald Clark
    免费加速器官网_免费加速器_免费加速器加速 - 云+社区 - 腾讯云:2021-10-31 · 科技企业加速器(EnterpriseAccelerator),是一种众快速成长企业为主要服务对象,通过服务模式创新充分满足企业对于空间、管理、服务、合作等方面个性化需求的新型空间载体和服务网络,具有较强的集群吸引力和创新网络形态。是介于企业孵化器和科技园区之间的一种中间业态。
    pdf / bibtex

    Academic Service

    Reviewer, CVPR, ICCV, BMVC, IJCV, TPAM, 手机vnp的服务器怎么填, IROS, RAL

    中国怎么上youtub

    Teaching Assistant, Computer Animation, University of Oxford (2015,2016,2017)

    Assistant Lecturer, Network Fundamentals, Univeristy of the Witwatersrand (2013)


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