# Poster: Reliable 3D Reconstruction for Ad-hoc Edge Implementations

arXiv: https://arxiv.org/abs/2411.10705 · HTML: https://mlsanity.com/paper/2411.10705/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Built on by (most cited first, 2 of 2)

- [Reliable Multi-view 3D Reconstruction for `Just-in-time' Edge Environments](https://mlsanity.com/paper/2508.15158/) (2025)
- [Reinforcement Learning-Driven Edge Management for Reliable Multi-view 3D Reconstruction](https://mlsanity.com/paper/2510.08839/) (2025)

## Abstract (arXiv)

Ad-hoc edge deployments to support real-time complex video processing applications such as, multi-view 3D reconstruction often suffer from spatio-temporal system disruptions that greatly impact reconstruction quality. In this poster paper, we present a novel portfolio theory-inspired edge resource management strategy to ensure reliable multi-view 3D reconstruction by accounting for possible system disruptions.