Edge Collaborative Gaussian Splatting with Integrated Rendering and Communication

• URL: http://arxiv.org/abs/2510.22718v1
• Date: Sun, 26 Oct 2025 15:33:29 GMT
• Title: Edge Collaborative Gaussian Splatting with Integrated Rendering and Communication
• Authors: Yujie Wan, Chenxuan Liu, Shuai Wang, Tong Zhang, James Jianqiao Yu, Kejiang Ye, Dusit Niyato, Chengzhong Xu,
• Abstract summary: We present edge collaborative (ECO-GS) where each user can switch between a small GS model to guarantee fidelity and a remote large GS model to guarantee fidelity.<n>We propose integrated and communication (IRAC) which jointly optimize low-cost rendering status and edge power allocation.
• Score: 69.23838350582764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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