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

arXiv: https://arxiv.org/abs/2602.00215 · HTML: https://mlsanity.com/paper/2602.00215/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fast Back-Projection for Non-Line of Sight Reconstruction](https://mlsanity.com/paper/1703.02016/) (2017)
- [Non-Line-of-Sight Reconstruction using Efficient Transient Rendering](https://mlsanity.com/paper/1809.08044/) (2018)
- [Recent Advances in Imaging Around Corners](https://mlsanity.com/paper/1910.05613/) (2019)
- [Steady-state Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1811.09910/) (2018)
- [Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge Occluder](https://mlsanity.com/paper/2006.09241/) (2020)
- [Optically lightweight tracking of objects around a corner](https://mlsanity.com/paper/1606.01873/) (2016)
- [Simultaneous whole-animal 3D-imaging of neuronal activity using light field microscopy](https://mlsanity.com/paper/1401.5333/) (2014)
- [Exploiting Occlusion in Non-Line-of-Sight Active Imaging](https://mlsanity.com/paper/1711.06297/) (2017)

## Abstract (arXiv)

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.