Valter Piedade

valter.piedade@tecnico.ulisboa.pt


PhD candidate in Electrical and Computer Engineering at Técnico Lisboa (University of Lisbon), affiliated with the Institute for Systems and Robotics (ISR-Lisboa), under the supervision of Dr. Pedro Miraldo and Prof. José Gaspar.

My research focuses on computer vision, particularly 3D reconstruction, SLAM, and robust estimation for real-world perception and localization. I develop algorithms and systems for visual localization and mapping, with an emphasis on real-time performance and robustness.


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  • 2026

Revisiting Monocular SLAM with Spatio-Temporal Scene Modeling
V. Piedade, L. Manam, M. Yamazaki, P. Miraldo
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

This work proposes a novel pipeline for real-time monocular V-SLAM, SLAM-MER, implemented from scratch in C++. SLAM-MER introduces a spatio-temporal querying of 3D points for improved camera localization, and a 3D-2D localization design that leverages recent feed-forward geometry estimators while enabling real-time performance.


  • 2025

SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity
V. Piedade, C. Sidhartha, J. Gaspar, V. M. Govindu, P. Miraldo
IEEE/CVF International Conference on Computer Vision (ICCV), 2025
Project page · Paper · Video · Poster
Highlight paper (2.3%)

SAC-GNC is a novel adaptive annealing strategy for Graduated Non-Convexity (GNC) which integrates sample consensus into the classical GNC framework. The addition of sample consensus improved robustness and reduced dependence on predefined parameters. The effectiveness of this approach was demonstrated in 3D registration and pose graph optimization tasks.


  • 2024

A Probability-guided Sampler for Neural Implicit Surface Rendering
G. D. Pais, V. Piedade, M. Chatterjee, M. Greiff, and P. Miraldo
European Conference on Computer Vision (ECCV), 2024
Project page · Paper · arXiv · Video · Poster

Novel pixel sampler for Neural Implicit Rendering that leverages the scene's Signed Distance Function (SDF). This approach achieves sharper reconstructions and improved performance in less-observed regions, without requiring additional prior data.


  • 2023

BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus
V. Piedade, P. Miraldo
IEEE/CVF International Conference on Computer Vision (ICCV), 2023
Project page · Paper · arXiv · GitHub · Video · Poster

BANSAC is a sampling strategy for RANSAC. We derived a dynamic Bayesian network that iteratively updates individual data points' inlier probability. In each iteration, the updated probabilities guide the sampling process. This approach outperforms the best baselines in accuracy, being also more efficient.

STORESLAM: Accurate Agent Localization and Mapping Methods for Structured Indoor Retail Store Environments

Visual SLAM system for an autonomous mobile robot using a single fisheye stereo camera in a retail store environment. The system was designed and implemented from scratch in C++.

Last updated on May 2, 2026.