Collaborative End-Edge-Cloud Framework for Multi-modal Dynamic 3D Reconstruction on Edge Devices

With the development of embodied intelligence, high-fidelity and real-time environmental perception has become a core challenge. Although the 3D Gaussian Spatter technology has achieved breakthroughs in rendering quality, it still faces issues such as mismatched computing power and "ghost" artifacts caused by dynamic objects on resource-constrained edge devices. This paper proposes a new perception framework: Firstly, a end-edge-cloud collaborative architecture is constructed, and computing-intensive global optimization is offloaded to the edge side or the cloud through dynamic task allocation; Secondly, the "learning-forgotten" mechanism is introduced, combining lightweight mask MLP and se-mantic assistance, to online identify and eliminate dynamic objects through Gaussian lifecycle management; Finally, through the multimodal tight coupling of laser radar, inertial measurement unit, and visual data, robust initialization is achieved. The test results in real scenarios show that this framework, while maintaining real-time rendering at the edge end, significantly improves reconstruction consistency and reduces trajectory errors.