Edge Collaborative Gaussian Splatting with Integrated Rendering and Communication
This addresses rendering quality issues for users on low-cost devices, but it is incremental as it builds on existing Gaussian splatting methods.
The paper tackles the problem of degraded rendering quality in Gaussian splatting on low-cost devices by proposing edge collaborative GS (ECO-GS), which allows switching between local and remote models, and achieves over 100x faster computational time with an imitation learning algorithm.
Gaussian splatting (GS) struggles with degraded rendering quality on low-cost devices. To address this issue, we present edge collaborative GS (ECO-GS), where each user can switch between a local small GS model to guarantee timeliness and a remote large GS model to guarantee fidelity. However, deciding how to engage the large GS model is nontrivial, due to the interdependency between rendering requirements and resource conditions. To this end, we propose integrated rendering and communication (IRAC), which jointly optimizes collaboration status (i.e., deciding whether to engage large GS) and edge power allocation (i.e., enabling remote rendering) under communication constraints across different users by minimizing a newly-derived GS switching function. Despite the nonconvexity of the problem, we propose an efficient penalty majorization minimization (PMM) algorithm to obtain the critical point solution. Furthermore, we develop an imitation learning optimization (ILO) algorithm, which reduces the computational time by over 100x compared to PMM. Experiments demonstrate the superiority of PMM and the real-time execution capability of ILO.