A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras

This paper proposes an observer for generating depth maps of a scene from a sequence of measurements acquired by a two-plane light-field (plenoptic) camera. The observer is based on a gradient-descent methodology. The use of motion allows for estimation of depth maps where the scene contains insufficient texture for static estimation methods to work. A rigourous analysis of stability of the observer error is provided, and the observer is tested in simulation, demonstrating convergence behaviour.Comment: Full version of paper submitted to CDC 2018. 11 pages. 12 figure

A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras

This paper proposes an observer for generating depth maps of a scene from a sequence of measurements acquired by a two-plane light-field (plenoptic) camera. The observer is based on a gradient-descent methodology. The use of motion allows for estimation of depth maps where the scene contains insufficient texture for static estimation methods to work. A rigourous analysis of stability of the observer error is provided, and the observer is tested in simulation, demonstrating convergence behaviour.This research was supported by the Australian Research Council through the ARC Discovery Project DP160100783 “Sensing a complex world: Infinite dimensional observer theory for robots.