Success Probability in Multi-View Imaging

TL;DR

This paper develops a probabilistic framework to analyze the success likelihood of multi-view imaging systems, considering camera pointing noise and overlap requirements, with applications to satellite formation design for cloud reconstruction.

Contribution

It introduces a new framework for analyzing success probability in multi-view imaging under pointing noise, linking system design parameters to probabilistic success.

Findings

Success probability depends on overlap and pointing accuracy.

Self-calibration can mitigate pointing errors if overlap is sufficient.

Framework applied to nanosatellite formation for cloud 3D reconstruction.

Abstract

Platforms such as robots, security cameras, drones and satellites are used in multi-view imaging for three-dimensional (3D) recovery by stereoscopy or tomography. Each camera in the setup has a field of view (FOV). Multi-view analysis requires overlap of the FOVs of all cameras, or a significant subset of them. However, the success of such methods is not guaranteed, because the FOVs may not sufficiently overlap. The reason is that pointing of a camera from a mount or platform has some randomness (noise), due to imprecise platform control, typical to mechanical systems, and particularly moving systems such as satellites. So, success is probabilistic. This paper creates a framework to analyze this aspect. This is critical for setting limitations on the capabilities of imaging systems, such as resolution (pixel footprint), FOV, the size of domains that can be captured, and efficiency. The framework uses the fact that imprecise pointing can be mitigated by self-calibration - provided that there is sufficient overlap between pairs of views and sufficient visual similarity of views. We show an example considering the design of a formation of nanosatellites that seek 3D reconstruction of clouds.