Pedro Miraldo

Pedro Miraldo
Mitsubishi Electric Research Labs
201 Broadway, Cambridge, MA
miraldo (at) merl (dot) com
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I am a Senior Principal Research Scientist at MERL Mitsubishi Electric Research Laboratories. Before joining MERL, I was a second-stage Researcher (comparable to an Assistant Research Professor) at the Institute for Systems & Robotics and the Department of Electrical & Computer Engineering, IST Instituto Superior Técnico, Lisboa. From 2018 to 2019, I was a postdoctoral associate at KTH Royal Institute of Technology. Previously, I held an FCT postdoctoral researcher grant (a highly competitive individual research grant) at IST.

I received my Master's and Ph.D. degrees in Electrical and Computer Engineering from the Faculty of Sciences and Technology, University of Coimbra, Portugal.

My main research topics are 3D computer vision, robot vision, and active vision.

News:

  • We have two openings for interns at MERL to work on Simultaneous Localization and Mapping, and Robust Estimation in Computer Vision. Please apply:
    Robust Estimation for Computer Vision Visual Simultaneous Localization and Mapping
  • Paper accepted at the International Conference on 3D Vision (3DV), 2025;
  • A paper accepted at the IEEE Robotics and Automation Letters (RA-L), 2024;
  • A paper accepted at the European Conference on Computer Vision (ECCV), 2024;
  • A paper accepted at IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024;
  • Paper accepted at the International Conference on 3D Vision (3DV), 2024;
  • Two papers accepted at IEEE/CVF International Conference on Computer Vision (ICCV), 2023;

Selected Projects and Publications:

Neural Implicit Surface Rendering [project]:
  • A Probability-guided Sampler for Neural Implicit Surface Rendering, from
    G. Dias Pais, Valter Piedade, Moitreya Chatterjee, Marcus Greiff, and Pedro Miraldo
    European Conference on Computer Vision (ECCV), 2024
    [doi, merl-tr, project, video, code]



Neural Radiance Fields for Dynamic Scenes [project]:
  • Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling, from
    X. Liu, Y-W Tai, C-K Tang, P. Miraldo, S. Lohit and M. Chatterjee,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2024
    [arXiv, merl-tr, video, code, doi]

    Highlight paper (2.8%)


Adaptive Sample Consensus [project]:
  • BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus, from
    V. Piedade and P. Miraldo,
    IEEE/CVF Int'l Conf. Computer Vision (ICCV), 2023.
    [arXiv, code, doi]


Frame-to-Frame Rotation Estimation in Crowded Scenes [project]:
  • Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes, from
    F. Delattre, D. Dirnfeld, P. Nguyen, S. Scarano, M. J. Jones, P. Miraldo, and E. Learned-Miller,
    IEEE/CVF Int'l Conf. Computer Vision (ICCV), 2023.
    [arXiv, code, dataset, doi]


Intersecting Lines for 3D Registration [project]:
  • Fast and Accurate 3D Registration from Line Intersection Constraints, from
    A. Mateus, S. Ranade, S. Ramalingam, and P. Miraldo,
    International Journal Computer Vision (IJCV), 2023. [doi, code]
  • Minimal Solvers for 3D Scan Alignment with Pairs of Intersecting Lines, from
    A. Mateus, S. Ramalingam, and P. Miraldo,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [cfv, doi, video, code]
  • Mapping of Sparse 3D Data using Alternating Projection, from
    S. Ranade, X. Yu, S. Kakkar, P. Miraldo, and S. Ramalingam,
    Asian Conf. Computer Vision (ACCV), 2020. [arXiv, video, doi]




Line Projections in Catadioptric Cameras:
  • A Unified Model for Line Projections in Catadioptric Cameras with Rotationally Symmetric Mirrors, from
    P. Miraldo and Jose Pedro Iglesias
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2022. [pdf,doi,code]
  • Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras, from
    Pedro Miraldo, Francisco Eiras, and Srikumar Ramalingam
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [arXiv:1804.09460,doi];


Active Structure-from-Motion using Lines:
  • An observer cascade for velocity and multiple line estimation, from
    A. Mateus, P. U. Lima, and P. Miraldo,
    IEEE Int'l Conf. Robotics and Automation (ICRA), 2022. [arXiv, doi]
  • On Incremental Structure-from-Motion using Lines, from
    A. Mateus, O. Tahri, A. P. Aguiar, P. U. Lima, and P. Miraldo,
    Transactions on Robotics (T-RO), 2021. [arXiv, doi]
  • Active Estimation of 3D Lines in Spherical Coordinates, from
    A. Mateus, O. Tahri, and P. Miraldo,
    American Control Conference (ACC), 2019. [arXiv, doi]
  • Active Structure-from-Motion for 3D Straight Lines, from
    A. Mateus, O. Tahri, and P. Miraldo,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), 2018. [link, video, doi]


3D Registration using Deep Learning [project]:
  • 3DRegNet: A Deep Neural Network for 3D Point Registration from
    G. D. Pais, S. Ramalingam, V. M. Govindu, J. C. Nascimento, R. Chellappa, and P. Miraldo,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [arXiv, doi, code]


Generalized Essential Matrix
  • On the Generalized Essential Matrix Correction: An efficient solution to the problem and its applications, from
    Pedro Miraldo and Joao R. Cardoso (2020),
    Journal of Mathematical Imaging and Vision (JMIV). [arXiv:1709.06328, doi]
  • Generalized Essential Matrix: Properties of the Singular Value Decomposition, from
    P. Miraldo and H. Araujo (2015),
    Image and Vision Computing (IVC). [pdf, doi]


Using Lines and Points for General Camera Pose Estimation:
  • A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines, from
    P. Miraldo, T. Dias, S. Ramalingam,
    European Conf. Computer Vision (ECCV), 2018.     [link,project,video]
  • Pose Estimation for General Cameras using Lines, from
    P. Miraldo, H. Araujo and N. Gonçalves,
    IEEE Trans. Cybermetic (Systems, Man, and Cybernetics, Part B), 2015. [pdf, doi, video]
  • Planar Pose Estimation for General Cameras using Known 3D Lines, from
    P. Miraldo and H. Araujo,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), 2014. [pdf, doi, video]


Smooth Camera Models: Modeling and Calibration
  • Calibration of Smooth Camera Models, from
    P. Miraldo and H. Araujo (2013),
    IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI). [pdf, appendix, doi]
  • Point-based Calibration Using a Parametric Representation of General Imaging Models, from
    P. Miraldo, H. Araujo, and J. Queiro (2011),
    IEEE Int'l Conf. Computer Vision (ICCV). [pdf, appendix, doi]

Last updated: November 06, 2024