Computer Graphics meets Estimation Theory: Parameter Estimation Lower Bounds for Plenoptic Imaging Systems

Abstract

This work focuses on assessing the information- theoretic limits of parameter estimation in plenoptic imaging systems, which are capable of providing substantially more information about a given scene than conventional cameras. We present a framework to compute lower bounds for parameter estimation from noisy plenoptic observations, and our particular focus is on indirect imaging problems, where the observations do not contain line-of-sight (LOS) 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, we numerically evaluate the Hammersley-Chapman-Robbins bound to establish fundamental lower limits on the variance of any unbiased estimators of the unknown parameters. We demonstrate the utility of our proposed framework on a few canonical estimation tasks.