Bayesian methods and Markov chain Monte Carlo algorithms for curve reconstruction and point cloud data analysis
Point-cloud data routinely captured by modern imaging and sensor technologies provide detailed geometric descriptions of objects and environments, but their analysis is hindered by large data volumes, localization noise, and missing information. In addition, existing point-cloud reconstruction pipelines typically return a single best-fit structure without uncertainty quantification. We introduce a fully Bayesian framework for representing point-cloud data and reconstructing closed curves, in which observed points are modeled as noisy perturbations of latent locations constrained to lie on the underlying curve that is regularized by a non-parametric prior. Posterior inference in our framew...
| Authors | Asir Intesar Tushar, Ioannis Sgouralis |
|---|---|
| Primary category | cs.LG |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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