StretchDenoise: Parametric Curve Reconstruction with Guarantees by Separating Connectivity from Residual Uncertainty of Samples

TL;DR

StretchDenoise is a fast, parameter-free method for reconstructing denoised, parametric curves from noisy 2D point clouds, separating connectivity recovery from high-frequency denoising to avoid over-smoothing and providing stochastic error guarantees.

Contribution

It introduces a novel two-pass approach that separates connectivity recovery from denoising, enabling guaranteed stochastic error bounds and improved reconstruction accuracy.

Findings

Operates efficiently on local neighborhoods

Guarantees stochastic error bounds for noisy samples

Improves reconstruction accuracy over ground truth

Abstract

We reconstruct a closed denoised curve from an unstructured and highly noisy 2D point cloud. Our proposed method uses a two- pass approach: Previously recovered manifold connectivity is used for ordering noisy samples along this manifold and express these as residuals in order to enable parametric denoising. This separates recovering low-frequency features from denoising high frequencies, which avoids over-smoothing. The noise probability density functions (PDFs) at samples are either taken from sensor noise models or from estimates of the connectivity recovered in the first pass. The output curve balances the signed distances (inside/outside) to the samples. Additionally, the angles between edges of the polygon representing the connectivity become minimized in the least-square sense. The movement of the polygon's vertices is restricted to their noise extent, i.e., a cut-off distance corresponding to a maximum variance of the PDFs. We approximate the resulting optimization model, which consists of higher-order functions, by a linear model with good correspondence. Our algorithm is parameter-free and operates fast on the local neighborhoods determined by the connectivity. We augment a least-squares solver constrained by a linear system to also handle bounds. This enables us to guarantee stochastic error bounds for sampled curves corrupted by noise, e.g., silhouettes from sensed data, and we improve on the reconstruction error from ground truth. Open source to reproduce figures and tables in this paper is available at: https://github.com/stefango74/stretchdenoise