A Renderer-Enabled Framework for Computing Parameter Estimation Lower Bounds in Plenoptic Imaging Systems

TL;DR

This paper introduces a rendering-based framework to compute fundamental lower bounds on the accuracy of scene parameter estimation in plenoptic imaging, using information theory and computer graphics to evaluate the limits of passive indirect imaging.

Contribution

It presents a novel framework combining rendering software and information-theoretic bounds to assess the fundamental limits of parameter estimation in plenoptic systems.

Findings

Lower bounds closely match the performance of maximum likelihood estimators.

The framework effectively evaluates the impact of rendering inaccuracies on estimation bounds.

Experimental results validate the bounds as indicative of fundamental limits in various scenarios.

Abstract

This work focuses on assessing the information-theoretic limits of scene parameter estimation in plenoptic imaging systems. A general framework to compute lower bounds on the parameter estimation error from noisy plenoptic observations is presented, with a particular focus on passive indirect imaging problems, where the observations do not contain line-of-sight information about the parameter(s) of interest. Using computer graphics rendering software to synthesize the often-complicated dependence among parameter(s) of interest and observations, i.e. the forward model, the proposed framework evaluates the Hammersley-Chapman-Robbins bound to establish lower bounds on the variance of any unbiased estimator of the unknown parameters. The effects of inexact rendering of the true forward model on the computed lower bounds are also analyzed, both theoretically and via simulations. Experimental evaluations compare the computed lower bounds with the performance of the Maximum Likelihood Estimator on a canonical object localization problem, showing that the lower bounds computed via the framework proposed here are indicative of the true underlying fundamental limits in several nominally representative scenarios.