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arXiv Machine Learning By Asir Intesar Tushar, Ioannis Sgouralis

Bayesian methods and Markov chain Monte Carlo algorithms for curve reconstruction and point cloud data analysis

Read the original on arXiv Machine Learning →

The paper presents a fully Bayesian framework for reconstructing closed curves from point‑cloud data, treating observed points as noisy perturbations of latent locations constrained to lie on an underlying curve. A non‑parametric prior regularizes the curve, and posterior inference is performed with Markov chain Monte Carlo samplers designed for point‑cloud characteristics. Experiments on synthetic and real LiDAR datasets demonstrate accurate reconstructions and quantified uncertainty over the recovered curves.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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