Pedro Miraldo

Pedro Miraldo
Pedro's Quick Links

  • Biography

    Pedro Miraldo held an FCT postdoctoral researcher grant at the Institute for Systems & Robotics and the Department of Electrical & Computer Engineering, IST Instituto Superior Tecnico Lisbon from 2014 to 2018. Then, he joined the Division of Decision and Control Systems at KTH Royal Institute of Technology as a postdoctoral associate from 2018 to 2019. Finally, he returned to IST in 2019 as a second-stage Researcher (comparable to Assistant Research Professor).

  • Recent News & Events

    •  NEWS    MERL Papers and Workshops at CVPR 2025
      Date: June 11, 2025 - June 15, 2025
      Where: Nashville, TN, USA
      MERL Contacts: Matthew Brand; Moitreya Chatterjee; Anoop Cherian; François Germain; Michael J. Jones; Toshiaki Koike-Akino; Jing Liu; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Naoko Sawada; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
      Brief
      • MERL researchers are presenting 2 conference papers, co-organizing two workshops, and presenting 7 workshop papers at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025 conference, which will be held in Nashville, TN, USA from June 11-15, 2025. CVPR is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:


        Main Conference Papers:

        1. "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing" by Y.H. Lai, J. Ebbers, Y. F. Wang, F. Germain, M. J. Jones, M. Chatterjee

        This work deals with the task of weakly‑supervised Audio-Visual Video Parsing (AVVP) and proposes a novel, uncertainty-aware algorithm called UWAV towards that end. UWAV works by producing more reliable segment‑level pseudo‑labels while explicitly weighting each label by its prediction uncertainty. This uncertainty‑aware training, combined with a feature‑mixup regularization scheme, promotes inter‑segment consistency in the pseudo-labels. As a result, UWAV achieves state‑of‑the‑art performance on two AVVP datasets across multiple metrics, demonstrating both effectiveness and strong generalizability.

        Paper: https://www.merl.com/publications/TR2025-072

        2. "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection" by Y. G. Jung, J. Park, J. Yoon, K.-C. Peng, W. Kim, A. B. J. Teoh, and O. Camps.

        This work tackles unsupervised anomaly detection in complex scenarios where normal data is noisy and has an unknown, imbalanced class distribution. Existing models face a trade-off between robustness to noise and performance on rare (tail) classes. To address this, the authors propose TailSampler, which estimates class sizes from embedding similarities to isolate tail samples. Using TailSampler, they develop TailedCore, a memory-based model that effectively captures tail class features while remaining noise-robust, outperforming state-of-the-art methods in extensive evaluations.

        paper: https://www.merl.com/publications/TR2025-077


        MERL Co-Organized Workshops:

        1. Multimodal Algorithmic Reasoning (MAR) Workshop, organized by A. Cherian, K.-C. Peng, S. Lohit, H. Zhou, K. Smith, L. Xue, T. K. Marks, and J. Tenenbaum.

        Workshop link: https://marworkshop.github.io/cvpr25/

        2. The 6th Workshop on Fair, Data-Efficient, and Trusted Computer Vision, organized by N. Ratha, S. Karanam, Z. Wu, M. Vatsa, R. Singh, K.-C. Peng, M. Merler, and K. Varshney.

        Workshop link: https://fadetrcv.github.io/2025/


        Workshop Papers:

        1. "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations" by N. Sawada, P. Miraldo, S. Lohit, T.K. Marks, and M. Chatterjee (Oral)

        With their ability to model object surfaces in a scene as a continuous function, neural implicit surface reconstruction methods have made remarkable strides recently, especially over classical 3D surface reconstruction methods, such as those that use voxels or point clouds. Towards this end, we propose FreBIS - a neural implicit‑surface framework that avoids overloading a single encoder with every surface detail. It divides a scene into several frequency bands and assigns a dedicated encoder (or group of encoders) to each band, then enforces complementary feature learning through a redundancy‑aware weighting module. Swapping this frequency‑stratified stack into an off‑the‑shelf reconstruction pipeline markedly boosts 3D surface accuracy and view‑consistent rendering on the challenging BlendedMVS dataset.

        paper: https://www.merl.com/publications/TR2025-074

        2. "Multimodal 3D Object Detection on Unseen Domains" by D. Hegde, S. Lohit, K.-C. Peng, M. J. Jones, and V. M. Patel.

        LiDAR-based object detection models often suffer performance drops when deployed in unseen environments due to biases in data properties like point density and object size. Unlike domain adaptation methods that rely on access to target data, this work tackles the more realistic setting of domain generalization without test-time samples. We propose CLIX3D, a multimodal framework that uses both LiDAR and image data along with supervised contrastive learning to align same-class features across domains and improve robustness. CLIX3D achieves state-of-the-art performance across various domain shifts in 3D object detection.

        paper: https://www.merl.com/publications/TR2025-078

        3. "Improving Open-World Object Localization by Discovering Background" by A. Singh, M. J. Jones, K.-C. Peng, M. Chatterjee, A. Cherian, and E. Learned-Miller.

        This work tackles open-world object localization, aiming to detect both seen and unseen object classes using limited labeled training data. While prior methods focus on object characterization, this approach introduces background information to improve objectness learning. The proposed framework identifies low-information, non-discriminative image regions as background and trains the model to avoid generating object proposals there. Experiments on standard benchmarks show that this method significantly outperforms previous state-of-the-art approaches.

        paper: https://www.merl.com/publications/TR2025-058

        4. "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector" by K. Li, T. Zhang, K.-C. Peng, and G. Wang.

        This work addresses challenges in 3D object detection for autonomous driving by improving the fusion of LiDAR and camera data, which is often hindered by domain gaps and limited labeled data. Leveraging advances in foundation models and prompt engineering, the authors propose PF3Det, a multi-modal detector that uses foundation model encoders and soft prompts to enhance feature fusion. PF3Det achieves strong performance even with limited training data. It sets new state-of-the-art results on the nuScenes dataset, improving NDS by 1.19% and mAP by 2.42%.

        paper: https://www.merl.com/publications/TR2025-076

        5. "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis" by Y. Ni., S. Wen, P. Konius, A. Cherian

        Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, two auxiliary consistency losses are porposed for diffusion fine-tuning. First, a prior consistency regularization loss ensures that the predicted diffusion noise for prior (non- subject) images remains consistent with that of the pretrained model, improving fidelity. Second, a subject consistency regularization loss enhances the fine-tuned model’s robustness to multiplicative noise modulated latent code, helping to preserve subject identity while improving diversity. Our experimental results demonstrate the effectiveness of our approach in terms of image diversity, outperforming DreamBooth in terms of CLIP scores, background variation, and overall visual quality.

        paper: https://www.merl.com/publications/TR2025-073

        6. "LatentLLM: Attention-Aware Joint Tensor Compression" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        We propose a new framework to convert a large foundation model such as large language models (LLMs)/large multi- modal models (LMMs) into a reduced-dimension latent structure. Our method uses a global attention-aware joint tensor decomposition to significantly improve the model efficiency. We show the benefit on several benchmark including multi-modal reasoning tasks.

        paper: https://www.merl.com/publications/TR2025-075

        7. "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine- tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this gap, by directly constructing a smaller model while guided by the downstream task. We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure. Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.

        paper: https://www.merl.com/publications/TR2025-079
    •  
    •  NEWS    MERL Papers and Workshops at CVPR 2024
      Date: June 17, 2024 - June 21, 2024
      Where: Seattle, WA
      MERL Contacts: Petros T. Boufounos; Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Jonathan Le Roux; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Jing Liu; Kuan-Chuan Peng; Pu (Perry) Wang; Ye Wang; Matthew Brand
      Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Machine Learning, Speech & Audio
      Brief
      • MERL researchers are presenting 5 conference papers, 3 workshop papers, and are co-organizing two workshops at the CVPR 2024 conference, which will be held in Seattle, June 17-21. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details of MERL contributions are provided below.

        CVPR Conference Papers:

        1. "TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models" by H. Ni, B. Egger, S. Lohit, A. Cherian, Y. Wang, T. Koike-Akino, S. X. Huang, and T. K. Marks

        This work enables a pretrained text-to-video (T2V) diffusion model to be additionally conditioned on an input image (first video frame), yielding a text+image to video (TI2V) model. Other than using the pretrained T2V model, our method requires no ("zero") training or fine-tuning. The paper uses a "repeat-and-slide" method and diffusion resampling to synthesize videos from a given starting image and text describing the video content.

        Paper: https://www.merl.com/publications/TR2024-059
        Project page: https://merl.com/research/highlights/TI2V-Zero

        2. "Long-Tailed Anomaly Detection with Learnable Class Names" by C.-H. Ho, K.-C. Peng, and N. Vasconcelos

        This work aims to identify defects across various classes without relying on hard-coded class names. We introduce the concept of long-tailed anomaly detection, addressing challenges like class imbalance and dataset variability. Our proposed method combines reconstruction and semantic modules, learning pseudo-class names and utilizing a variational autoencoder for feature synthesis to improve performance in long-tailed datasets, outperforming existing methods in experiments.

        Paper: https://www.merl.com/publications/TR2024-040

        3. "Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling" by X. Liu, Y-W. Tai, C-T. Tang, P. Miraldo, S. Lohit, and M. Chatterjee

        This work presents a new strategy for rendering dynamic scenes from novel viewpoints. Our approach is based on stratifying the scene into regions based on the extent of motion of the region, which is automatically determined. Regions with higher motion are permitted a denser spatio-temporal sampling strategy for more faithful rendering of the scene. Additionally, to the best of our knowledge, ours is the first work to enable tracking of objects in the scene from novel views - based on the preferences of a user, provided by a click.

        Paper: https://www.merl.com/publications/TR2024-042

        4. "SIRA: Scalable Inter-frame Relation and Association for Radar Perception" by R. Yataka, P. Wang, P. T. Boufounos, and R. Takahashi

        Overcoming the limitations on radar feature extraction such as low spatial resolution, multipath reflection, and motion blurs, this paper proposes SIRA (Scalable Inter-frame Relation and Association) for scalable radar perception with two designs: 1) extended temporal relation, generalizing the existing temporal relation layer from two frames to multiple inter-frames with temporally regrouped window attention for scalability; and 2) motion consistency track with a pseudo-tracklet generated from observational data for better object association.

        Paper: https://www.merl.com/publications/TR2024-041

        5. "RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation" by Z. Yang, J. Liu, P. Chen, A. Cherian, T. K. Marks, J. L. Roux, and C. Gan

        We leverage Large Language Models (LLM) for zero-shot semantic audio visual navigation. Specifically, by employing multi-modal models to process sensory data, we instruct an LLM-based planner to actively explore the environment by adaptively evaluating and dismissing inaccurate perceptual descriptions.

        Paper: https://www.merl.com/publications/TR2024-043

        CVPR Workshop Papers:

        1. "CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation" by R. Dey, B. Egger, V. Boddeti, Y. Wang, and T. K. Marks

        This paper proposes a new method for generating 3D faces and rendering them to images by combining the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN.

        Paper: https://www.merl.com/publications/TR2024-045

        2. “Tracklet-based Explainable Video Anomaly Localization” by A. Singh, M. J. Jones, and E. Learned-Miller

        This paper describes a new method for localizing anomalous activity in video of a scene given sample videos of normal activity from the same scene. The method is based on detecting and tracking objects in the scene and estimating high-level attributes of the objects such as their location, size, short-term trajectory and object class. These high-level attributes can then be used to detect unusual activity as well as to provide a human-understandable explanation for what is unusual about the activity.

        Paper: https://www.merl.com/publications/TR2024-057

        MERL co-organized workshops:

        1. "Multimodal Algorithmic Reasoning Workshop" by A. Cherian, K-C. Peng, S. Lohit, M. Chatterjee, H. Zhou, K. Smith, T. K. Marks, J. Mathissen, and J. Tenenbaum

        Workshop link: https://marworkshop.github.io/cvpr24/index.html

        2. "The 5th Workshop on Fair, Data-Efficient, and Trusted Computer Vision" by K-C. Peng, et al.

        Workshop link: https://fadetrcv.github.io/2024/

        3. "SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models" by X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand, G. Wang, and T. Koike-Akino

        This paper proposes a generalized framework called SuperLoRA that unifies and extends different variants of low-rank adaptation (LoRA). Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance up to 10-fold gain in parameter efficiency for transfer learning tasks.

        Paper: https://www.merl.com/publications/TR2024-062
    •  

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  • Research Highlights

  • Internships with Pedro

    • CV0063: Internship - Visual Simultaneous Localization and Mapping

      MERL is looking for a self-motivated graduate student to work on Visual Simultaneous Localization and Mapping (V-SLAM). Based on the candidate’s interests, the intern can work on a variety of topics such as (but not limited to): camera pose estimation, feature detection and matching, visual-LiDAR data fusion, pose-graph optimization, loop closure detection, and image-based camera relocalization. The ideal candidate would be a PhD student with a strong background in 3D computer vision and good programming skills in C/C++ and/or Python. The candidate must have published at least one paper in a top-tier computer vision, machine learning, or robotics venue, such as CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS. The intern will collaborate with MERL researchers to derive and implement new algorithms for V-SLAM, conduct experiments, and report findings. A submission to a top-tier conference is expected. The duration of the internship and start date are flexible.

      Required Specific Experience

      • Experience with 3D Computer Vision and Simultaneous Localization & Mapping.

    See All Internships at MERL

  • MERL Publications

    •  Sawada, N., Miraldo, P., Lohit, S., Marks, T.K., Chatterjee, M., "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations", IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR), June 2025.
      BibTeX TR2025-074 PDF
      • @inproceedings{Sawada2025jun,
      • author = {Sawada, Naoko and Miraldo, Pedro and Lohit, Suhas and Marks, Tim K. and Chatterjee, Moitreya},
      • title = {{FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-074}
      • }
    •  Ranade, S., Pais, G., Whitaker, R., Nascimento, J., Miraldo, P., Ramalingam, S., "SurfR: Surface Reconstruction with Multi-scale Attention", International Conference on 3D Vision (3DV), March 2025.
      BibTeX TR2025-039 PDF Presentation
      • @inproceedings{Ranade2025mar,
      • author = {{{Ranade, Siddhant and Pais, Goncalo and Whitaker, Ross and Nascimento, Jacinto and Miraldo, Pedro and Ramalingam, Srikumar}}},
      • title = {{{SurfR: Surface Reconstruction with Multi-scale Attention}}},
      • booktitle = {International Conference on 3D Vision (3DV)},
      • year = 2025,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2025-039}
      • }
    •  Pais, G., Piedade, V., Chatterjee, M., Greiff, M., Miraldo, P., "A Probability-guided Sampler for Neural Implicit Surface Rendering", European Conference on Computer Vision (ECCV), Leonardis, A. and Ricci, E. and Roth, S. and Russakovsky, O., Sattler, T. and Varol, G., Eds., DOI: 10.1007/​978-3-031-72913-3_10, September 2024, pp. 164-182.
      BibTeX TR2024-129 PDF Video
      • @inproceedings{Pais2024sep,
      • author = {Pais, Goncalo and Piedade, Valter and Chatterjee, Moitreya and Greiff, Marcus and Miraldo, Pedro},
      • title = {{A Probability-guided Sampler for Neural Implicit Surface Rendering}},
      • booktitle = {European Conference on Computer Vision (ECCV)},
      • year = 2024,
      • editor = {Leonardis, A. and Ricci, E. and Roth, S. and Russakovsky, O., Sattler, T. and Varol, G.},
      • pages = {164--182},
      • month = sep,
      • publisher = {Springer, Cham},
      • doi = {10.1007/978-3-031-72913-3_10},
      • isbn = {978-3-031-72913-3},
      • url = {https://www.merl.com/publications/TR2024-129}
      • }
    •  Roque, P., Miraldo, P., Dimarogonas, D., "Multi-Agent Formation Control using Epipolar Constraints", IEEE Robotics and Automation Letters, DOI: 10.1109/​LRA.2024.3444690, Vol. 9, No. 12, pp. 11002-11009, September 2024.
      BibTeX TR2024-147 PDF
      • @article{Roque2024sep,
      • author = {Roque, Pedro and Miraldo, Pedro and Dimarogonas, Dimos},
      • title = {{Multi-Agent Formation Control using Epipolar Constraints}},
      • journal = {IEEE Robotics and Automation Letters},
      • year = 2024,
      • volume = 9,
      • number = 12,
      • pages = {11002--11009},
      • month = sep,
      • doi = {10.1109/LRA.2024.3444690},
      • issn = {2377-3766},
      • url = {https://www.merl.com/publications/TR2024-147}
      • }
    •  Liu, X., Tai, Y.-W., Tang, C.-K., Miraldo, P., Lohit, S., Chatterjee, M., "Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), May 2024, pp. 19667-19679.
      BibTeX TR2024-042 PDF Videos Software
      • @inproceedings{Liu2024may,
      • author = {Liu, Xinhang and Tai, Yu-wing and Tang, Chi-Keung and Miraldo, Pedro and Lohit, Suhas and Chatterjee, Moitreya},
      • title = {{Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2024,
      • pages = {19667--19679},
      • month = may,
      • publisher = {IEEE},
      • url = {https://www.merl.com/publications/TR2024-042}
      • }
    See All MERL Publications for Pedro

  • Other Publications

    •  André Mateus, Omar Tahri, A. Pedro Aguiar, Pedro U. Lima and Pedro Miraldo, "On Incremental Structure-from-Motion using Lines", IEEE Trans. Robotics (T-RO), Vol. 38, No. 1, pp. 391-406, 2022.
      BibTeX
      • @Article{j32,
      • author = {Mateus, Andr\'e and Tahri, Omar and Aguiar, A. Pedro and Lima, Pedro U. and Pedro Miraldo},
      • title = {On Incremental Structure-from-Motion using Lines},
      • journal = {IEEE Trans. Robotics (T-RO)},
      • year = 2022,
      • volume = 38,
      • number = 1,
      • pages = {391--406},
      • note = {[\href{https://arxiv.org/abs/2105.11196}{arXiv:2105.11196}, \href{https://doi.org/10.1109/TRO.2021.3085487}{doi}]}
      • }
    •  João R. Cardoso and Pedro Miraldo, "Solving the discrete Euler-Arnold equations for the generalized rigid body motion", Journal of Computational and Applied Mathematics (CAM), Vol. 402, pp. 113814, 2022.
      BibTeX
      • @Article{j33,
      • author = {Cardoso, Jo{\~a}o R. and Pedro Miraldo},
      • title = {Solving the discrete Euler-Arnold equations for the generalized rigid body motion},
      • journal = {Journal of Computational and Applied Mathematics (CAM)},
      • year = 2022,
      • volume = 402,
      • pages = 113814,
      • note = {[\href{https://arxiv.org/abs/2109.00505}{\it arXiv:2109.00505}, \href{https://doi.org/10.1016/j.cam.2021.113814}{doi}]}
      • }
    •  André Mateus, Pedro U. Lima and Pedro Miraldo, "An observer cascade for velocity and multiple line estimation", IEEE Int'l Conf. Robotics and Automation (ICRA), 2022.
      BibTeX
      • @Inproceedings{j34,
      • author = {Mateus, Andr\'e and Lima, Pedro U. and Pedro Miraldo},
      • title = {An observer cascade for velocity and multiple line estimation},
      • booktitle = {IEEE Int'l Conf. Robotics and Automation (ICRA)},
      • year = 2022,
      • note = {[\href{https://arxiv.org/abs/2203.01879}{arXiv:2203.01879},{doi}]}
      • }
    •  R. T. Rodrigues, P. Miraldo, D. V. Dimarogonas and A. P. Aguiar, "Active Depth Estimation: Stability Analysis and its Applications", IEEE Int'l Conf. Robotics and Automation (ICRA), 2020, pp. 2002-2008.
      BibTeX
      • @Inproceedings{j26,
      • author = {Rodrigues, R. T. and P. Miraldo and Dimarogonas, D. V. and Aguiar, A. P.},
      • title = {Active Depth Estimation: Stability Analysis and its Applications},
      • booktitle = {IEEE Int'l Conf. Robotics and Automation (ICRA)},
      • year = 2020,
      • pages = {2002--2008},
      • note = {[\href{https://arxiv.org/abs/2003.07137}{\it arXiv:2003.07137},\href{https://doi.org/10.1109/ICRA40945.2020.9196670}{doi}]}
      • }
    •  G. Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C. Nascimento, Rama Chellappa and Pedro Miraldo, "3DRegNet: A Deep Neural Network for 3D Point Registration", IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2020, pp. 7191-7201.
      BibTeX
      • @Inproceedings{j27,
      • author = {Pais, G. Dias and Ramalingam, Srikumar and Govindu, Venu Madhav and Nascimento, Jacinto C. and Chellappa, Rama and Pedro Miraldo},
      • title = {3DRegNet: A Deep Neural Network for 3D Point Registration},
      • booktitle = {IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR)},
      • year = 2020,
      • pages = {7191--7201},
      • note = {[\href{https://arxiv.org/abs/1904.01701}{\it arXiv:1904.01701},\href{https://doi.org/10.1109/CVPR42600.2020.00722}{doi}]}
      • }
    •  André Mateus, Srikumar Ramalingam and Pedro Miraldo, "Minimal Solvers for 3D Scan Alignment with Pairs of Intersecting Lines", IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2020, pp. 7232-7242.
      BibTeX
      • @Inproceedings{j28,
      • author = {Mateus, Andr{\'e} and Ramalingam, Srikumar and Pedro Miraldo},
      • title = {Minimal Solvers for 3D Scan Alignment with Pairs of Intersecting Lines},
      • booktitle = {IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR)},
      • year = 2020,
      • pages = {7232--7242},
      • note = {[\href{https://doi.org/10.1109/CVPR42600.2020.00726}{doi}]}
      • }
    •  Pedro Miraldo and João R. Cardoso, "On the Generalized Essential Matrix Correction: An efficient solution to the problem and its applications", Journal of Mathematical Imaging and Vision, Vol. 62, pp. 1107-1120, 2020.
      BibTeX
      • @Article{j29,
      • author = {Pedro Miraldo and Cardoso, Jo{\~a}o R.},
      • title = {On the Generalized Essential Matrix Correction: An efficient solution to the problem and its applications},
      • journal = {Journal of Mathematical Imaging and Vision},
      • year = 2020,
      • volume = 62,
      • pages = {1107--1120},
      • note = {[\href{https://arxiv.org/abs/1709.06328}{\it arXiv:1709.06328}, \href{https://doi.org/10.1007/s10851-020-00961-w}{doi}]}
      • }
    •  Pedro Roque, Elisa Bin, Pedro Miraldo and Dimos V. Dimarogonas, "Fast Model Predictive Image-Based Visual Servoing for Quadrotors", IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), 2020, pp. 7566-7572.
      BibTeX
      • @Inproceedings{j30,
      • author = {Roque, Pedro and Bin, Elisa and Pedro Miraldo and Dimarogonas, Dimos V.},
      • title = {Fast Model Predictive Image-Based Visual Servoing for Quadrotors},
      • booktitle = {IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS)},
      • year = 2020,
      • pages = {7566--7572},
      • note = {[\href{https://doi.org/10.1109/IROS45743.2020.9340759}{doi}]}
      • }
    •  Siddhant Ranade, Yu Xin, Shantnu Kakkar, Pedro Miraldo and Srikumar Ramalingam, "Mapping of Sparse 3D Data using Alternating Projection", Asian Conf. Computer Vision (ACCV), 2020, pp. 295-313.
      BibTeX
      • @Inproceedings{j31,
      • author = {Ranade, Siddhant and Xin, Yu and Kakkar, Shantnu and Pedro Miraldo and Ramalingam, Srikumar},
      • title = {Mapping of Sparse 3D Data using Alternating Projection},
      • booktitle = {Asian Conf. Computer Vision (ACCV)},
      • year = 2020,
      • pages = {295--313},
      • note = {[\href{https://arxiv.org/abs/2010.02516}{\it arXiv:2010.02516},\href{https://doi.org/10.1007/978-3-030-69525-5_18}{doi}]}
      • }
    •  J. Campos, J. R. Rodrigues and P. Miraldo, "POSEAMM: A Unified Framework for Solving Pose Problems using an Alternating Minimization Method", IEEE Int'l Conf. Robotics and Automation (ICRA), 2019, pp. 3493-3499.
      BibTeX
      • @Inproceedings{j21,
      • author = {Campos, J. and Rodrigues, J. R. and P. Miraldo},
      • title = {POSEAMM: A Unified Framework for Solving Pose Problems using an Alternating Minimization Method},
      • booktitle = {IEEE Int'l Conf. Robotics and Automation (ICRA)},
      • year = 2019,
      • pages = {3493--3499},
      • note = {[\href{https://arxiv.org/abs/1904.04858}{\it arXiv:1904.04858}, \href{https://doi.org/10.1109/ICRA.2019.8793694}{doi}]}
      • }
    •  G. Pais, J. C. Nascimento and P. Miraldo, "OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras", IEEE Int'l Conf. Robotics and Automation (ICRA), 2019, pp. 4782-4789.
      BibTeX
      • @Inproceedings{j22,
      • author = {Pais, G. and Nascimento, J. C. and P. Miraldo},
      • title = {OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras},
      • booktitle = {IEEE Int'l Conf. Robotics and Automation (ICRA)},
      • year = 2019,
      • pages = {4782--4789},
      • note = {[\href{https://arxiv.org/abs/1903.00676}{\it arXiv:1903.00676}, \href{https://doi.org/10.1109/ICRA.2019.8794471}{doi}]}
      • }
    •  P. Miraldo, S. Saha and S. Ramalingam, "Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment", IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9691-9700.
      BibTeX
      • @Inproceedings{j23,
      • author = {P. Miraldo and Saha, S. and Ramalingam, S.},
      • title = {Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment},
      • booktitle = {IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR)},
      • year = 2019,
      • pages = {9691--9700},
      • note = {[\href{https://arxiv.org/abs/1904.03941}{\it arXiv:1904.03941}, \href{https://doi.org/10.1109/CVPR.2019.00993}{doi}]}
      • }
    •  R. Rodrigues, P. Miraldo, D. V. Dimarogonas and A. P. Aguiar, "A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision", IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), 2019, pp. 3719-3724.
      BibTeX
      • @Inproceedings{j24,
      • author = {Rodrigues, R. and P. Miraldo and Dimarogonas, D. V. and Aguiar, A. P.},
      • title = {A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision},
      • booktitle = {IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS)},
      • year = 2019,
      • pages = {3719--3724},
      • note = {[\href{https://arxiv.org/abs/1908.00309}{\it arXiv:1908.00309}, \href{https://doi.org/10.1109/IROS40897.2019.8968459}{doi}]}
      • }
    •  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, "SocRob$@$Home Integrating AI Components in a Domestic Robot System", Künstliche Intelligenz (KI), Vol. 33, No. 4, pp. 343-356, 2019.
      BibTeX
      • @Article{j25,
      • author = {Lima, P. U. and Azevedo, C. and Brzozowska, E. and Cartucho, J. and Dias, T. J. and Gon\c{c}alves, J. and Kinarullathil, M. and Lawless, G. and Lima, O. and Luz, R. and P. Miraldo and Piazza, E. and Silva, M. and Veiga, T. and Ventura, R.},
      • title = {SocRob$@$Home Integrating AI Components in a Domestic Robot System},
      • journal = {K\"{u}nstliche Intelligenz (KI)},
      • year = 2019,
      • volume = 33,
      • number = 4,
      • pages = {343--356},
      • note = {[\href{https://doi.org/10.1007/s13218-019-00618-w}{doi}]}
      • }
    •  X. Liu, Z. Li, K. Zhong, Y. Chao, P. Miraldo and Y. Shi, "Generic distortion model for metrology under optical microscopes", Optics and Lasers in Engineering (OLEN), Vol. 103, pp. 119-126, 2018.
      BibTeX
      • @Article{j15,
      • author = {Liu, X. and Li, Z. and Zhong, K. and Chao, Y. and P. Miraldo and Shi, Y.},
      • title = {Generic distortion model for metrology under optical microscopes},
      • journal = {Optics and Lasers in Engineering (OLEN)},
      • year = 2018,
      • volume = 103,
      • pages = {119--126},
      • note = {[\href{https://doi.org/10.1016/j.optlaseng.2017.12.006}{doi}]}
      • }
    •  R. Rodrigues, M. Basiri, A. P. Aguiar and P. Miraldo, "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), Vol. 3, No. 3, pp. 2079-2086, 2018.
      BibTeX
      • @Article{j16,
      • author = {Rodrigues, R. and Basiri, M. and Aguiar, A. P. and P. Miraldo},
      • title = {Low-level Active Visual Navigation: Increasing robustness of vision-based localization using potential fields},
      • journal = {IEEE Robotics and Automation Letters (RA-L) and IEEE Int'l Conf. Robotics and Automation (ICRA)},
      • year = 2018,
      • volume = 3,
      • number = 3,
      • pages = {2079--2086},
      • note = {[\href{https://arxiv.org/abs/1801.07249}{\it arXiv:1801.07249}, \href{https://doi.org/10.1109/LRA.2018.2809628}{doi}]}
      • }
    •  P. Miraldo, F. Eiras and S. Ramalingam, "Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras", IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2018, pp. 2012-2021.
      BibTeX
      • @Inproceedings{j17,
      • author = {P. Miraldo and Eiras, F. and Ramalingam, S.},
      • title = {Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras},
      • booktitle = {IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR)},
      • year = 2018,
      • pages = {2012--2021},
      • note = {[\href{https://arxiv.org/abs/1804.09460}{\it arXiv:1804.09460}, \href{https://doi.org/10.1109/CVPR.2018.00215}{doi}]}
      • }
    •  A. Mateus, O. Tahri and P. Miraldo, "Active Structure-from-Motion for 3D Straight Lines", IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), 2018, pp. 5819-5825.
      BibTeX
      • @Inproceedings{j18,
      • author = {Mateus, A. and Tahri, O. and P. Miraldo},
      • title = {Active Structure-from-Motion for 3D Straight Lines},
      • booktitle = {IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS)},
      • year = 2018,
      • pages = {5819--5825},
      • note = {[\href{https://arxiv.org/abs/1807.00753}{\it arXiv:1807.00753}, \href{https://doi.org/10.1109/IROS.2018.8593793}{doi}]}
      • }
    •  P. Miraldo, T. Dias and S. Ramalingam, "A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines", European Conf. Computer Vision (ECCV), 2018, pp. 490-507.
      BibTeX
      • @Inproceedings{j19,
      • author = {P. Miraldo and Dias, T. and Ramalingam, S.},
      • title = {A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines},
      • booktitle = {European Conf. Computer Vision (ECCV)},
      • year = 2018,
      • pages = {490--507},
      • note = {[\href{https://arxiv.org/abs/1807.09970}{\it arXiv:1807.09970}, \href{https://doi.org/10.1007/978-3-030-01270-0_29}{doi}]}
      • }
    •  A. Mateus, D. Ribeiro, P. Miraldo and J. C. Nascimento, "Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation", Robotics and Autonomous Systems (RAS), Vol. 113, pp. 23-37, 2018.
      BibTeX
      • @Article{j20,
      • author = {Mateus, A. and Ribeiro, D. and P. Miraldo and Nascimento, J. C.},
      • title = {Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation},
      • journal = {Robotics and Autonomous Systems (RAS)},
      • year = 2018,
      • volume = 113,
      • pages = {23--37},
      • note = {[\href{https://arxiv.org/abs/1607.04441}{\it arXiv:1607.04441}, \href{https://doi.org/10.1016/j.robot.2018.12.007}{doi}]}
      • }
    •  X. Liu, Z. Li, P. Miraldo, K. Zhong and Y. Shi, "A framework to calibrate the scanning electron microscope under any magnifications", IEEE Photonics Technology Letters (PT-L), Vol. 28, No. 16, pp. 1715-1718, 2016.
      BibTeX
      • @Article{j12,
      • author = {Liu, X. and Li, Z. and P. Miraldo and Zhong, K. and Shi, Y.},
      • title = {A framework to calibrate the scanning electron microscope under any magnifications},
      • journal = {IEEE Photonics Technology Letters (PT-L)},
      • year = 2016,
      • volume = 28,
      • number = 16,
      • pages = {1715--1718},
      • note = {[\href{https://doi.org/10.1109/LPT.2016.2522758}{doi}]}
      • }
    •  T. Veiga, P. Miraldo, R. Ventura and P. Lima, "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), 2016, pp. 2745-2750.
      BibTeX
      • @Inproceedings{j13,
      • author = {Veiga, T. and P. Miraldo and Ventura, R. and Lima, P.},
      • title = {Efficient Object Search for Mobile Robots in Dynamic Environments: Semantic Map as an Input for the Decision Maker},
      • booktitle = {IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS)},
      • year = 2016,
      • pages = {2745--2750},
      • note = {[\href{https://doi.org/10.1109/IROS.2016.7759426}{doi}]}
      • }
    •  F. Amigoni, J. Berghofer, A. Bonarini, N. Hochgeschwender G. Fontana, L. Iocchi, G. K. Kraetzschmar, P. Lima, M. Matteucci, P. Miraldo, D. Nardi and V. Schiaonati, "Competitions for Benchmarking: Task and Functionality Scoring Complete Performance Assessment", IEEE Robotics Automation Magazine (RA-M), Vol. 22, No. 3, pp. 53-61, 2015.
      BibTeX
      • @Article{j10,
      • author = {Amigoni, F. and Berghofer, J. and Bonarini, A. and G. Fontana, N. Hochgeschwender and Iocchi, L. and Kraetzschmar, G. K. and Lima, P. and Matteucci, M. and P. Miraldo and Nardi, D. and Schiaonati, V.},
      • title = {Competitions for Benchmarking: Task and Functionality Scoring Complete Performance Assessment},
      • journal = {IEEE Robotics Automation Magazine (RA-M)},
      • year = 2015,
      • volume = 22,
      • number = 3,
      • pages = {53--61},
      • note = {[\href{https://doi.org/10.1109/MRA.2015.2448871}{doi}]}
      • }
    •  P. Miraldo and H. Araujo, "Direct Solution to the Minimal Generalized Pose", IEEE Trans. Cybernetics (T-CYB), Vol. 45, No. 3, pp. 404-415, 2015.
      BibTeX
      • @Article{j5,
      • author = {P. Miraldo and Araujo, H.},
      • title = {Direct Solution to the Minimal Generalized Pose},
      • journal = {IEEE Trans. Cybernetics (T-CYB)},
      • year = 2015,
      • volume = 45,
      • number = 3,
      • pages = {404--415},
      • note = {[\href{https://doi.org/10.1109/TCYB.2014.2326970}{doi}]}
      • }
    •  P. Miraldo, H. Araujo and N. Gonçalves, "Pose Estimation for General Cameras Using Lines", IEEE Trans. Cybernetics (T-CYB), Vol. 45, No. 10, pp. 2156-2164, 2015.
      BibTeX
      • @Article{j6,
      • author = {P. Miraldo and Araujo, H. and Gon\c{c}alves, N.},
      • title = {Pose Estimation for General Cameras Using Lines},
      • journal = {IEEE Trans. Cybernetics (T-CYB)},
      • year = 2015,
      • volume = 45,
      • number = 10,
      • pages = {2156--2164},
      • note = {[\href{https://doi.org/10.1109/TCYB.2014.2366378}{doi}]}
      • }
    •  P. Miraldo and H. Araujo, "Generalized Essential Matrix: Properties of the Singular Value Decomposition", Image and Vision Computing (IVC), Vol. 34, pp. 45-50, 2015.
      BibTeX
      • @Article{j7,
      • author = {P. Miraldo and Araujo, H.},
      • title = {Generalized Essential Matrix: Properties of the Singular Value Decomposition},
      • journal = {Image and Vision Computing (IVC)},
      • year = 2015,
      • volume = 34,
      • pages = {45--50},
      • note = {[\href{https://doi.org/10.1016/j.imavis.2014.11.003}{doi}]}
      • }
    •  P. Miraldo and H. Araujo, "Pose Estimation for Non-Central Cameras Using Planes", Springer J. Intelligent & Robotic Systems (JINT), Vol. 80, No. 3, pp. 595-608, 2015.
      BibTeX
      • @Article{j8,
      • author = {P. Miraldo and Araujo, H.},
      • title = {Pose Estimation for Non-Central Cameras Using Planes},
      • journal = {Springer J. Intelligent \& Robotic Systems (JINT)},
      • year = 2015,
      • volume = 80,
      • number = 3,
      • pages = {595--608},
      • note = {[\href{https://doi.org/10.1007/s10846-015-0193-3}{doi}]}
      • }
    •  P. Miraldo and H. Araujo, "A Simple and Robust Solution to the Minimal General Pose Estimation", IEEE Int'l Conf. Robotics and Automation (ICRA), 2014, pp. 2119-2125.
      BibTeX
      • @Inproceedings{j3,
      • author = {P. Miraldo and Araujo, H.},
      • title = {A Simple and Robust Solution to the Minimal General Pose Estimation},
      • booktitle = {IEEE Int'l Conf. Robotics and Automation (ICRA)},
      • year = 2014,
      • pages = {2119--2125},
      • note = {[\href{https://doi.org/10.1109/ICRA.2014.6907150}{doi}]}
      • }
    •  P. Miraldo and H. Araujo, "Planar Pose Estimation for General Cameras using Known 3D Lines", IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS), 2014, pp. 4234-4240.
      BibTeX
      • @Inproceedings{j4,
      • author = {P. Miraldo and Araujo, H.},
      • title = {Planar Pose Estimation for General Cameras using Known 3D Lines},
      • booktitle = {IEEE/RSJ Int'l Conf. Intelligent Robots and Systems (IROS)},
      • year = 2014,
      • pages = {4234--4240},
      • note = {[\href{https://doi.org/10.1109/IROS.2014.6943159}{doi}]}
      • }
    •  P. Miraldo and H. Araujo, "Calibration of Smooth Camera Models", IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI), Vol. 35, No. 9, pp. 2091-2103, 2013.
      BibTeX
      • @Article{j2,
      • author = {P. Miraldo and Araujo, H.},
      • title = {Calibration of Smooth Camera Models},
      • journal = {IEEE Trans. Pattern Analysis and Machine Intelligence (T-PAMI)},
      • year = 2013,
      • volume = 35,
      • number = 9,
      • pages = {2091--2103},
      • note = {[\href{https://doi.org/10.1109/TPAMI.2012.258}{doi}]}
      • }
    •  P. Miraldo, H. Araujo and J. Queiró, "Point-based Calibration Using a Parametric Representation of General Imaging Models", IEEE/CVF Int'l Conf. Computer Vision (ICCV), 2011, pp. 2304-2311.
      BibTeX
      • @Inproceedings{j1,
      • author = {P. Miraldo and Araujo, H. and Queir\'{o}, J.},
      • title = {Point-based Calibration Using a Parametric Representation of General Imaging Models},
      • booktitle = {IEEE/CVF Int'l Conf. Computer Vision (ICCV)},
      • year = 2011,
      • pages = {2304--2311},
      • note = {[\href{https://doi.org/10.1109/ICCV.2011.6126511}{doi}]}
      • }

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