# End-to-End Latency Optimization of Multi-view 3D Reconstruction for Disaster Response

arXiv: https://arxiv.org/abs/2304.01488 · HTML: https://mlsanity.com/paper/2304.01488/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild](https://mlsanity.com/paper/1803.05536/) (2018)

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

- [Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications](https://mlsanity.com/paper/2512.15823/) (2025)
- [Reinforcement Learning-Driven Edge Management for Reliable Multi-view 3D Reconstruction](https://mlsanity.com/paper/2510.08839/) (2025)

## Abstract (arXiv)

In order to plan rapid response during disasters, first responder agencies often adopt `bring your own device' (BYOD) model with inexpensive mobile edge devices (e.g., drones, robots, tablets) for complex video analytics applications, e.g., 3D reconstruction of a disaster scene. Unlike simpler video applications, widely used Multi-view Stereo (MVS) based 3D reconstruction applications (e.g., openMVG/openMVS) are exceedingly time consuming, especially when run on such computationally constrained mobile edge devices. Additionally, reducing the reconstruction latency of such inherently sequential algorithms is challenging as unintelligent, application-agnostic strategies can drastically degrade the reconstruction (i.e., application outcome) quality making them useless. In this paper, we aim to design a latency optimized MVS algorithm pipeline, with the objective to best balance the end-to-end latency and reconstruction quality by running the pipeline on a collaborative mobile edge environment. The overall optimization approach is two-pronged where: (a) application optimizations introduce data-level parallelism by splitting the pipeline into high frequency and low frequency reconstruction components and (b) system optimizations incorporate task-level parallelism to the pipelines by running them opportunistically on available resources with online quality control in order to balance both latency and quality. Our evaluation on a hardware testbed using publicly available datasets shows upto ~54% reduction in latency with negligible loss (~4-7%) in reconstruction quality.