发表机构
University of Tennessee（田纳西大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对点云数据量大、含噪声及缺失信息，现有重建无不确定性量化的问题，提出全贝叶斯框架与适配的MCMC采样器，经合成及LiDAR数据实验，实现精确曲线重建与不确定性量化。
AI 中文摘要
现代成像与传感器技术常规采集的点云数据可提供物体与环境的详细几何描述，但其分析受限于数据量大、定位噪声及信息缺失问题。此外，现有点云重建流程通常仅返回单一最优拟合结构，未进行不确定性量化。本文提出一种用于表示点云数据及重建闭合曲线的全贝叶斯框架，其中观测点被建模为潜在位置的带噪扰动，潜在位置受限于由非参数先验正则化的底层曲线。该框架中的后验推断采用一系列适配点云特性的马尔可夫链蒙特卡洛采样器执行。数值实验（含合成示例与真实世界LiDAR数据集）表明，所提方法可实现精确的曲线重建，并对恢复的曲线给出不确定性量化结果。
英文摘要
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 framework is carried out using a series of Markov chain Monte Carlo samplers tailored to point-cloud characteristics. Numerical experiments, including synthetic examples and real-world LiDAR datasets, show accurate reconstructions and quantified uncertainty over the recovered curves.
Comments32 pages and 12 figures
