RocSync: Millisecond-Accurate Temporal Synchronization for Heterogeneous Camera Systems

TL;DR

RocSync introduces a low-cost, versatile method for millisecond-accurate synchronization of diverse camera systems using a custom LED clock, enhancing multi-view applications in uncontrolled environments.

Contribution

It presents a novel LED-based visual encoding technique for precise synchronization across heterogeneous cameras without specialized hardware.

Findings

Achieves 1.34 ms residual synchronization error.

Outperforms existing light-, audio-, and timecode-based methods.

Improves downstream tasks like pose estimation and 3D reconstruction.

Abstract

Accurate spatiotemporal alignment of multi-view video streams is essential for a wide range of dynamic-scene applications such as multi-view 3D reconstruction, pose estimation, and scene understanding. However, synchronizing multiple cameras remains a significant challenge, especially in heterogeneous setups combining professional and consumer-grade devices, visible and infrared sensors, or systems with and without audio, where common hardware synchronization capabilities are often unavailable. This limitation is particularly evident in real-world environments, where controlled capture conditions are not feasible. In this work, we present a low-cost, general-purpose synchronization method that achieves millisecond-level temporal alignment across diverse camera systems while supporting both visible (RGB) and infrared (IR) modalities. The proposed solution employs a custom-built \textit{LED Clock} that encodes time through red and infrared LEDs, allowing visual decoding of the exposure window (start and end times) from recorded frames for millisecond-level synchronization. We benchmark our method against hardware synchronization and achieve a residual error of 1.34~ms RMSE across multiple recordings. In further experiments, our method outperforms light-, audio-, and timecode-based synchronization approaches and directly improves downstream computer vision tasks, including multi-view pose estimation and 3D reconstruction. Finally, we validate the system in large-scale surgical recordings involving over 25 heterogeneous cameras spanning both IR and RGB modalities. This solution simplifies and streamlines the synchronization pipeline and expands access to advanced vision-based sensing in unconstrained environments, including industrial and clinical applications.