# Wireless Software Synchronization of Multiple Distributed Cameras

arXiv: https://arxiv.org/abs/1812.09366 · HTML: https://mlsanity.com/paper/1812.09366/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 12 of 12)

- [Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image](https://mlsanity.com/paper/2110.05655/) (2021)
- [DiVa-360: The Dynamic Visual Dataset for Immersive Neural Fields](https://mlsanity.com/paper/2307.16897/) (2023)
- [AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results](https://mlsanity.com/paper/2508.16830/) (2025)
- [Efficient Hybrid Zoom using Camera Fusion on Mobile Phones](https://mlsanity.com/paper/2401.01461/) (2024)
- [Multi-view data capture for dynamic object reconstruction using handheld augmented reality mobiles](https://mlsanity.com/paper/2103.07883/) (2021)
- [Sub-millisecond Video Synchronization of Multiple Android Smartphones](https://mlsanity.com/paper/2107.00987/) (2021)
- [Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras](https://mlsanity.com/paper/2401.14132/) (2024)
- [SUPER: Selfie Undistortion and Head Pose Editing with Identity Preservation](https://mlsanity.com/paper/2406.12700/) (2024)
- [AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications](https://mlsanity.com/paper/2501.10396/) (2024)
- [SmartDepthSync: Open Source Synchronized Video Recording System of Smartphone RGB and Depth Camera Range Image Frames with Sub-millisecond Precision](https://mlsanity.com/paper/2111.03552/) (2021)
- [Self-Aligning Depth-regularized Radiance Fields for Asynchronous RGB-D Sequences](https://mlsanity.com/paper/2211.07459/) (2022)
- [Deep learning-based stereo camera multi-video synchronization](https://mlsanity.com/paper/2303.12916/) (2023)

## Abstract (arXiv)

We present a method for precisely time-synchronizing the capture of image sequences from a collection of smartphone cameras connected over WiFi. Our method is entirely software-based, has only modest hardware requirements, and achieves an accuracy of less than 250 microseconds on unmodified commodity hardware. It does not use image content and synchronizes cameras prior to capture. The algorithm operates in two stages. In the first stage, we designate one device as the leader and synchronize each client device's clock to it by estimating network delay. Once clocks are synchronized, the second stage initiates continuous image streaming, estimates the relative phase of image timestamps between each client and the leader, and shifts the streams into alignment. We quantitatively validate our results on a multi-camera rig imaging a high-precision LED array and qualitatively demonstrate significant improvements to multi-view stereo depth estimation and stitching of dynamic scenes. We release as open source 'libsoftwaresync', an Android implementation of our system, to inspire new types of collective capture applications.