RocSync: Millisecond-Accurate Temporal Synchronization for Heterogeneous Camera Systems

2025-11-18

AI Summary

Heterogeneous camera systems—e.g., visible-light/infrared, professional/consumer-grade, or audio-equipped/audio-less setups—lack hardware synchronization in real-world scenarios, leading to significant spatiotemporal misalignment across multi-view videos. Method: This paper proposes a vision-based time-encoding method leveraging a custom-designed LED Clock. By embedding temporal exposure timestamps within frames using red and infrared LEDs, the approach achieves cross-modal, audio-free, and external-timecode-free millisecond-level synchronization. It further integrates RMSE-optimized temporal alignment with joint multi-device calibration. Contribution/Results: The method reduces synchronization residuals to 1.34 ms—substantially outperforming existing optical signal, audio-based, and timecode synchronization schemes. Validated in large-scale surgical recordings involving over 25 heterogeneous cameras, it significantly improves downstream tasks including multi-view 3D reconstruction and pose estimation.

Technology Category

• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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 extit{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.

Problem

• Synchronizing heterogeneous camera systems lacking hardware sync
• Achieving millisecond temporal alignment for RGB and IR cameras
• Enabling accurate multi-view applications in unconstrained real-world environments

Innovation

• LED Clock encodes time via red and infrared LEDs
• Visual decoding of exposure window from recorded frames
• Millisecond-level synchronization for heterogeneous camera systems