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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 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. |
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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. |
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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 - 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. |
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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 February 2, 2026.