Efficient Nonparametric Estimation of 3D Point Cloud Signals through Distributed Learning.

ABSTRACT: Advancements in technology have elevated the prominence of 3D point cloud data, making its analysis increasingly vital across various applications. This need drives the demand for advanced statistical analytic approaches to handle challenges such as size, sparsity, and irregularity in 3D point clouds while ensuring accurate and efficient information extraction. This paper introduces a novel nonparametric distributed (NPD) learning framework that utilizes trivariate spline smoothing over a triangulation of domain. The proposed NPD algorithm features a straightforward, scalable, and communication-efficient implementation scheme that can achieve near-linear speedup. In addition, we provide rigorous theoretical support for the NPD estimation framework and demonstrate that the NPD spline estima

SUBMITTER: Wang G 

PROVIDER: S-EPMC12148237 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature