RocSync: Millisecond-Accurate Temporal Synchronization for Heterogeneous Camera Systems

• URL: http://arxiv.org/abs/2511.14948v1
• Date: Tue, 18 Nov 2025 22:13:06 GMT
• Title: RocSync: Millisecond-Accurate Temporal Synchronization for Heterogeneous Camera Systems
• Authors: Jaro Meyer, Frédéric Giraud, Joschua Wüthrich, Marc Pollefeys, Philipp Fürnstahl, Lilian Calvet,
• Abstract summary: We present a low-cost, general-purpose synchronization method that achieves millisecond-level temporal alignment across diverse camera systems.<n>The proposed solution employs a custom-built itLED Clock that encodes time through red and infrared, allowing visual decoding of the exposure window.<n>We validate the system in large-scale surgical recordings involving over 25 heterogeneous cameras spanning both IR and RGB modalities.
• Score: 38.099313678683224
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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