Pedro Miraldo MERL Website

Pedro Miraldo

Pedro Miraldo
Mitsubishi Electric Research Labs
201 Broadway, Cambridge, MA
miraldo (at) merl (dot) com

I am a Senior Principal Research Scientist at Mitsubishi Electric Research Laboratories (MERL). My research lies at the intersection of computer vision, robotics, and artificial intelligence, with a particular emphasis on 3D perception for autonomous systems. My current research focuses on real-time localization and mapping, 3D and 4D scene reconstruction, and robust estimation, combining classical geometric methods with modern learning-based approaches. More broadly, my work has addressed problems in camera modeling and calibration, pose estimation, Simultaneous Localization And Mapping (SLAM), Structure-from-Motion (SfM), 3D registration, and robust geometric estimation.

Before joining MERL, I was a second-stage Researcher (comparable to an Assistant Research Professor) at the Institute for Systems and Robotics and the Department of Electrical and Computer Engineering at Instituto Superior Técnico (IST), Lisbon. From 2018 to 2019, I was a Postdoctoral Associate at KTH Royal Institute of Technology. Previously, I held an FCT postdoctoral research fellowship, a 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.

News:

  • Will serve as Associate Editor (Localization and Mapping) for ICRA 2027
  • Gave a talk at RPL Summer School at KTH Royal Institute of Technology, with the title Revisiting Visual Simultaneous Localization and Mapping
  • Three papers accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026
  • Paper accepted to the Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.

Selected Projects and Publications:

Illustration for Graduated Non-Convexity
Simultaneous Localization and Mapping [project]:
  • Revisiting Monocular SLAM with Spatio-Temporal Scene Modeling, from
    Valter Piedade, Lalit Manam, Masashi Yamazaki, and Pedro Miraldo
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
    [paper, project, video, code, docs]


Illustration for Graduated Non-Convexity
Feed-forward Depth Estimator [project]:
  • Point4Cast: Streaming Dynamic Scene Reconstruction and Forecasting, from
    X. Liu, P. Miraldo, S. Lohit, H. Jiang, N. Sawada, Y-W Tai, C-K Tang, M. Chatterjee
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
    [paper, project]



Illustration for Graduated Non-Convexity
Graduated Non-Convexity [project]:
  • SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity, from
    V. Piedade, C. Sidhartha, J. Gaspar, V. M. Govindu, and P. Miraldo
    IEEE/CVF Int'l Conf. Computer Vision (ICCV), 2025
    [paper link, video, code]



Illustration for Neural Implicit Surface Rendering
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]



Illustration for Neural Radiance Fields for Dynamic Scenes
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]



Illustration for Adaptive Sample Consensus
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]


Illustration for Frame-to-Frame Rotation Estimation in Crowded Scenes
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]


Illustration for Intersecting Lines for 3D Registration
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 of 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.
    [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]




Illustration for Line Projections in Catadioptric Cameras
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];


Illustration for Active Structure-from-Motion using Lines
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, doi]


Illustration for 3D Registration using Deep Learning
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]


Illustration for Generalized Essential Matrix
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]


Illustration for General Camera Pose Estimation using Lines and Points
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, video]
  • Pose Estimation for General Cameras using Lines, from
    P. Miraldo, H. Araujo and N. Gonçalves,
    IEEE Trans. Cybernetics (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]


Illustration for General Camera Calibration
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: Sep 7, 2026