# Success Probability in Multi-View Imaging

arXiv: https://arxiv.org/abs/2407.21027 · HTML: https://mlsanity.com/paper/2407.21027/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 15 Jul 2024.

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

- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [A Survey on Deep Learning Techniques for Stereo-based Depth Estimation](https://mlsanity.com/paper/2006.02535/) (2020)
- [RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions](https://mlsanity.com/paper/2310.15171/) (2023)
- [Leveraging Vision Reconstruction Pipelines for Satellite Imagery](https://mlsanity.com/paper/1910.02989/) (2019)
- [Gravitationally Lensed Black Hole Emission Tomography](https://mlsanity.com/paper/2204.03715/) (2022)
- [Multi-sensor large-scale dataset for multi-view 3D reconstruction](https://mlsanity.com/paper/2203.06111/) (2022)
- [Multi-view polarimetric scattering cloud tomography and retrieval of droplet size](https://mlsanity.com/paper/2005.11423/) (2020)
- [Volumetric Cloud Field Reconstruction](https://mlsanity.com/paper/2311.17657/) (2023)
- [GEOSCAN: Global Earth Observation using Swarm of Coordinated Autonomous Nanosats](https://mlsanity.com/paper/2111.15627/) (2021)

## Abstract (arXiv)

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.