Pedro Miraldo Publications

2026

  1. Valter Piedade, Lalit Manam, Masashi Yamazaki, Pedro Miraldo,
    Revisiting Monocular SLAM with Spatio-Temporal Scene Modeling,
    IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026, pp. [arXiv, page, slam-mer, doi];
  2. Tianye Ding, Yiming Xie, Yiqing Liang, Moitreya Chatterjee, Pedro Miraldo, Huaizu Jiang,
    LASER: Layer-wise Scale Alignment for Training-Free Streaming 4D Reconstruction,
    IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026, pp. [arXiv, project, code];
  3. Xinhang Liu, Pedro Miraldo, Suhas Lohit, Huaizu Jiang, Naoko Sawada, Yu-Wing Tai, Chi-Keung Tang, Moitreya Chatterjee,
    Point4Cast: Streaming Dynamic Scene Reconstruction and Forecasting,
    IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026, pp. [link];

2025

  1. S. Kato, R. Yataka, P. Wang, P. Miraldo, T. Fujihashi, P. Boufounos,
    RAPTR: Radar-based 3D Pose Estimation using Transformer,
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2025, pp. [arXiv, code];
  2. V. Piedade, C. Sidhartha, J. Gaspar, V. M. Govindu, and P. Miraldo,
    SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity,
    IEEE/CVF Int'l Conf. Computer Vision (ICCV), 2025, pp. 5780-5790 Highlight (2.3%) [paper, link, video, doi, code (soon)];
  3. Sawada Naoko, Pedro Miraldo, Suhas Lohit, Tim K. Marks, and Moitreya Chatterjee,
    FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations,
    CV4Metaverse 2025 workshop paper track of the 4th Computer Vision for Metaverse Workshop at CVPR 2025, pp. [doi, arXiv];
  4. Siddhant Ranade, Gonçalo Dias Pais, Ross Tyler Whitaker, Jacinto Nascimento, Pedro Miraldo, and Srikumar Ramalingam,
    SurfR: Surface Reconstruction with Multi-scale Attention,
    Int'l Conf. 3D Vision (3DV), 2024, pp. 556-566 [arXiv, doi];

2024

  1. G. Dias Pais, Valter Piedade, Marcus Greiff, Moitreya Chatterjee, and Pedro Miraldo
    A Probability-guided Sampler for Neural Implicit Surface Rendering,
    European Conference on Computer Vision (ECCV), 2024, pp. 164-182 [doi, merl-tr, project, video, doi];
  2. Pedro Roque, Pedro Miraldo, Dimos V. Dimarogonas
    Multi-Agent Formation Control using Epipolar Constraints,
    IEEE Robotics and Automation Letters (RA-L), 9(12):11002-11009 [doi, code];
  3. Xinhang Liu, Yu-Wing Tai, Chi-Keung Tang, Pedro Miraldo, Suhas Lohit, and Moitreya Chatterjee
    Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2024 Highlight (2.8%) [arXiv, merl-tr, video, code, doi];
  4. Arihant Gaur, G. Dias Pais, and Pedro Miraldo
    Oriented-grid Encoder for 3D Implicit Representations,
    Int'l Conf. 3D Vision (3DV), 2024, pp. 1208-1218, [arXiv, doi];

2023

  1. Valter Piedade and Pedro Miraldo
    BANSAC: A dynamic BAyesian Network for SAmple Consensus,
    IEEE/CVF Int'l Conf. Computer Vision (ICCV), 2023 [arXiv, code, doi];
  2. Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, and Erik Learned-Miller
    Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes,
    IEEE/CVF Int'l Conf. Computer Vision (ICCV), 2023 [arXiv, code, dataset, doi];
  3. Yuri Shimane, Pedro Miraldo, Karl Berntorp, Marcus Greiff, Purnanand Elango, Avishai Weiss
    High-Fidelity Simulation of Horizon-Based Optical Navigation with Open-Source Software,
    International Astronautical Congress (IAC), 2023 [merl-tr]
  4. Karl Berntorp, Marcus Greiff, Stefano Di Cairano, and Pedro Miraldo
    Bayesian Sensor Fusion for Joint Vehicle Localization and Road Mapping Using Onboard Sensors,
    International Conference on Information Fusion (FUSION), 2023 [merl-tr, doi];
  5. Helder Araujo, Pedro Miraldo, and Nathan Crombez,
    Localization and Navigation with Omnidirectional Images,
    Omnidirectional Vision: From Theory to Applications,
    John Wiley & Sons (2023). [ISBN: 978-1-394-25643-3];
  6. Andre Mateus, Siddhant Ranade, Srikumar Ramalingam, and Pedro Miraldo
    Fast and Accurate 3D Registration from Line Intersection Constraints,
    International Journal of Computer Vision (IJCV), 131:2044-2069, 2023 [merl-tr, code, doi];

2022

  1. Pedro Miraldo and Jose Pedro Iglesias (2022),
    A Unified Model for Line Projections in Catadioptric Cameras with Rotationally Symmetric Mirrors,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2022, pp. 15776-15785 [pdf, code, doi];
  2. Andre Mateus, Pedro U. Lima, and Pedro Miraldo (2022),
    An observer cascade for velocity and multiple line estimation,
    IEEE Int'l Conf. Robotics and Automation (ICRA), pp. 9418-9424 [arXiv:2203.01879, doi];

2021

  1. João R. Cardoso and Pedro Miraldo (2021),
    Solving the Discrete Euler-Arnold Equations for the Generalized Rigid Body Motion,
    Journal of Computational and Applied Mathematics, 402:113814, [arXiv:2109.00505, doi];
  2. Andre Mateus, Omar Tahri, A. Pedro Aguiar, Pedro U. Lima, and Pedro Miraldo (2021),
    On Incremental Structure-from-Motion using Lines,
    IEEE Transactions on Robotics (T-RO), pp. [arXiv:2105.11196, doi];

2020

  1. Siddhant Ranade, Xin Yu, Shantnu Kakkar, Pedro Miraldo, and Srikumar Ramalingam (2020),
    Mapping of Sparse 3D Data using Alternating Projection,
    Asian Conf. Computer Vision (ACCV), pp. 295-313 [arXiv:2010.02516, doi];
  2. Pedro Roque, Elisa Bin, Pedro Miraldo, and Dimos V. Dimarogonas (2020),
    Fast Model Predictive Image-Based Visual Servoing for Quadrotors,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp. 7566-7572 [doi];
  3. Andre Mateus, Srikumar Ramalingam, and Pedro Miraldo (2020),
    Minimal Solvers for 3D Scan Alignment with Pairs of Intersecting Lines,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), pp. 7232-7242 [link, doi];
  4. G. Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C. Nascimento, Rama Chellappa, and Pedro Miraldo (2020),
    3DRegNet: A Deep Neural Network for 3D Point Registration,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), pp. 7191-7201 [arXiv:1904.01701, doi];
  5. Pedro Miraldo and Joao R. Cardoso (2020),
    On the Generalized Essential Matrix Correction: An efficient solution to the problem and its applications,
    Journal of Mathematical Imaging and Vision (JMIV), 62:1107-1120 [arXiv:1709.06328, doi];
  6. Rômulo T. Rodrigues, Pedro Miraldo, Dimos V. Dimarogonas, A. Pedro Aguiar (2020),
    Active Depth Estimation: Stability Analysis and its Applications,
    IEEE Int'l Conf. Robotics and Automation (ICRA), pp. 2002-2008 [arXiv:2003.07137, doi];

2019

  1. P. U. Lima, C. Azevedo, E. Brzozowska, J. Cartucho, T. J. Dias, J. Gonçalves, M. Kinarullathil,
    G. Lawless, O. Lima, R. Luz, P. Miraldo, E. Piazza, M. Silva, T. Veiga, and R. Ventura (2019),
    SocRob@Home: Integrating AI Components in a Domestic Robot System,
    Künstliche Intelligenz (KI), pp. 1-14 [doi];
  2. R. T. Rodrigues, Pedro Miraldo, D. V. Dimarogonas, A. P. Aguiar (2019),
    A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp. 3719-3724 [arXiv:1908.00309, doi];
  3. P. Miraldo, S. Saha, and S. Ramalingam (2019),
    Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), pp. 9691-9700 [arXiv:1904.03941, doi];
  4. A. Mateus, O. Tahri, and P. Miraldo (2019),
    Active Estimation of 3D Lines in Spherical Coordinates,
    American Control Conference (ACC), pp. 3950-3955 [arXiv:1902.00473, doi];
  5. G. Pais, T. J. Dias, J. Nascimento, and P. Miraldo (2019),
    OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras,
    IEEE Int'l Conf. Robotics and Automation (ICRA), pp. 4782-4789 [arXiv:1903.00676, doi];
  6. J. Campos, J. R. Cardoso, and P. Miraldo (2019),
    POSEAMM: A Unified Framework for Solving Pose Problems using an Alternating Minimization Method,
    IEEE Int'l Conf. Robotics and Automation (ICRA), pp. 3493-3499 [arXiv:1904.04858, code, doi];
  7. A. Mateus, D. Ribeiro, P. Miraldo, and J. C. Nascimento (2019),
    Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation,
    Robotics and Autonomous Systems Journal (RAS), 113:23-37, [arXiv:1607.04441, doi];

2018

  1. P. Miraldo, T. Dias, S. Ramalingam (2018),
    A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines,
    European Conf. Computer Vision (ECCV), pp. 490-507 [arXiv:1807.09970, doi];
  2. A. Mateus, O. Tahri, and P. Miraldo (2018),
    Active Structure-from-Motion for 3D Straight Lines,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp. 5819-5825 [arXiv:1807.00753, doi];
  3. P. Miraldo, F. Eiras, and S. Ramalingam (2018),
    Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras,
    IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), pp. 2012-2021 [arXiv:1804.09460, doi];
  4. R. T. Rodrigues, M. Basiri, A. P. Aguiar, and P. Miraldo (2018),
    Low-level Active Visual Navigation:
    Increasing robustness of vision-based localization using potential fields    ,
    IEEE Robotics and Automation Letters (RA-L), and IEEE Int'l Conf. Robotics and Automation (ICRA), 3(3):2079-2086 [arXiv:1801.07249, doi];
  5. X. Liu, Z. Li, K. Zhong, Y. Chao, P. Miraldo, and Y. Shi (2018),
    Generic distortion model for metrology under optical microscopes,
    Optics and Lasers in Engineering (OLEN) 103:119-126 [doi];

2017

  1. L. Iocchi, G. Kraetzschmar, D. Nardi, P. U. Lima, P. Miraldo, E. Bastianelli, and R. Capobianco (2017),
    RoCKIn@Home: Domestic Robots Challenge,
    RoCKIn - Benchmarking Through Robot Competitions, IntechOpen [ISBN: 978-953-51-3374-2];
  2. R. Rodrigues, M. Basiri, A. P. Aguiar, and P. Miraldo (2017),
    Feature Based Potential Field for Low-level Active Visual Navigation,
    Iberian Robotics Conf. (ROBOT), pp. 791-800 [arXiv:1709.04687, doi];
  3. D. Ribeiro, A. Mateus, P. Miraldo, and J. C. Nascimento (2017),
    A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation,
    IEEE Int'l Conf. on Autonomous Robot Systems and Competitions (ICARSC), pp. 165-171 [arXiv:1607.04436, doi];

2016

  1. J. Cardoso, P. Miraldo, and H. Araujo (2016),
    Plücker correction problem: Analysis and improvements in efficiency,
    IEEE/IAPR Int'l Conf. on Pattern Recognition (ICPR), pp. 2796-2801 [pdf, doi];
  2. J. Iglesias, P. Miraldo, and R. Ventura (2016),
    Towards an Omnidirectional Catadioptric RGB-D Camera,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp. 2506-2513 [pdf, doi];
  3. T. Veiga, P. Miraldo, R. Ventura, and P. Lima (2016),
    Efficient Object Search for Mobile Robots in Dynamic Environments:
    Semantic Map as an Input for the Decision Maker    ,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp. 2745-2750 [pdf, doi];
  4. R. Ventura, M. Basiri, A. Mateus, J. Garcia, P. Miraldo, P. Santos, and P. U. Lima (2016),
    A Domestic Assistive Robot Developed Through Robotic Competitions,
    WS Autonomous Mobile Service Robots joint with
    Int'l Joint Conference on Artificial Intelligence (IJCAI) [pdf];
  5. T. Dias, H. Araujo, and P. Miraldo (2016),
    3D Reconstruction with Low-Resolution, High Radial Distortion Stereo Images,
    ACM Int'l Conf. on Distributed Smart Cameras (ICDSC), pp. 98-103 [pdf, doi];
  6. T. Dias, P. Miraldo, and N. Gonçalves (2016),
    Augmented Reality on Non-Central Catadioptric Camera Systems,
    Journal of Intelligent & Robotic Systems (JINT), 83(3):359-373 [pdf, doi];
  7. X. Liu, Z. Li, P. Miraldo, K. Zhong, and Y. Shi (2016),
    A Framework to Calibrate the Scanning Electron Microscope under Variational Magnifications,
    IEEE Photonics Technology Letters, 28(16):1715-1718 [doi];

2015

  1. F. Amigoni, E. Bastianelli, J. Berghofer, A. Bonarini, G. Fontana, N. Hochgeschwender,
    L. Iocchi, G. K. Kraetzschmar, P. Lima, M. Matteucci, P. Miraldo, D. Nardi, V. Schiaffonati (2015),
    Enabling Replicable Experiments and Benchmarking with RoCKIn Competitions,
    IEEE Robotics and Automation Magazine (RAM), 22(3):53-61 [doi];
  2. A. Mateus, P. Miraldo, P. Lima, and J. Sequeira (2015),
    Human-Aware Navigation using External Omnidirectional Cameras,
    Iberian Robotics Conf. (ROBOT), pp. 283-295 [pdf, doi];
  3. T. Dias, P. Miraldo, N. Gonçalves, and P. Lima (2015),
    Augmented Reality on Robot Navigation using Non-Central Catadioptric Cameras,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp. 4999-5004 [pdf, doi];
  4. P. Miraldo and H. Araujo (2015),
    Pose Estimation for Non-Central Cameras Using Planes,
    Journal of Intelligent & Robotic Systems (JINT), 80(3):595-608 [pdf, doi]
  5. T. Dias, P. Miraldo, and N. Gonçalves (2015),
    A Framework for Augmented Reality using Non-Central Catadioptric Cameras,
    IEEE Int'l Conf. on Autonomous Robot Systems and Competitions (ICARSC), pp. 213-220 [pdf, doi];
  6. P. Miraldo and H. Araujo (2015),
    Generalized Essential Matrix: Properties of the Singular Value Decomposition,
    Image and Vision Computing (IVC), 34:45-50 [pdf, doi];
  7. P. Miraldo, H. Araujo and N. Gonçalves (2015),
    Pose Estimation for General Cameras using Lines,
    IEEE Trans. Cybernetics (Systems, Man, and Cybernetics, Part B), 45(10):2156-2164 [pdf, doi];

2014

  1. P. Miraldo and H. Araujo (2014),
    Direct Solution to the Minimal Generalized Pose,
    IEEE Trans. Cybernetics (Systems, Man, and Cybernetics, Part B), 45(3):404-415 [pdf, doi];
  2. P. Miraldo and H. Araujo (2014),
    Planar Pose Estimation for General Cameras using Known 3D Lines,
    IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), pp. 4234-4240 [pdf, doi];
  3. P. Miraldo and H. Araujo (2014),
    Pose Estimation for Non-Central Cameras Using Planes,
    IEEE Int'l Conf. on Autonomous Robot Systems and Competitions (ICARSC), pp. 104-109 [pdf, doi];
  4. P. Miraldo and H. Araujo (2014),
    A Simple and Robust Solution to the Minimal General Pose Estimation,
    IEEE Int'l Conf. Robotics and Automation (ICRA), pp. 2119-2125 [pdf, doi];

Before 2014

  1. P. Miraldo and H. Araujo (2013),
    Calibration of Smooth Camera Models,
    IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI), 35(9):2091-2103 [pdf, appendix, doi];
  2. P. Miraldo, H. Araujo, and J. Queiro (2011),
    Point-based Calibration Using a Parametric Representation of General Imaging Models,
    IEEE Int'l Conf. Computer Vision (ICCV), pp. 2304-2311 [pdf, appendix, doi];
  3. P. Miraldo and H. Araujo (2010)
    Improving the Resolution of the Generic Camera Model by Means of a Parametric Representation,
    Portuguese Conf. Automatic Control (CONTROLO);
  4. P. Miraldo and H. Araujo (2008),
    Gestures Interpretation Using Computer Vision for Human-Machine Interaction,
    Portuguese Conf. Pattern Recognition (RECPAD);

Thesis

  1. P. Miraldo (2013). General Camera Models: Calibration and Pose,
    PhD in Electrical Engineering, University of Coimbra [pdf];
  2. P. Miraldo (2008), "Interpretação de Gestos Usando Visão por Computador para Interacção Homem Máquina"
    MSc in Electrical and Computer Engineering, University of Coimbra.