Research and Projects
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Projects:
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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 in a retail store environment using a single fisheye stereo camera.
The system is being designed and implemented from scratch in C++.
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Publications:
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BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus
Valter Piedade, Pedro Miraldo
IEEE/CVF International Conference on Computer Vision (ICCV), 2023
Project page
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Paper
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arXiv
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Video
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Poster
BANSAC is a sampling strategy for RANSAC.
We derive 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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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
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Paper
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Video
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Poster
Novel pixel sampler for Neural Implicit Rendering that leverages the scene's Signed Distance Function (SDF).
Our approach achieves sharper reconstructions and improved performance in less-observed regions,
without requiring additional prior data.
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Last updated on October 20, 2024. Website source code from Jon Barron.
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