We investigate the feasibility of real-time 3D Gaussian Splatting (3DGS) rasterisation on edge clients with varying Gaussian splat counts and GPU computational budgets. Instead of evaluating multiple physical devices, we adopt an emulation-based approach that approximates different GPU capability tiers on a single high-end GPU. By systematically under-clocking the GPU core frequency and applying power caps, we emulate a controlled range of floating-point performance levels that approximate different GPU capability tiers. At each point in this range, we measure frame rate, runtime behaviour, and power consumption across scenes of varying complexity, pipelines, and optimisations, enabling analysis of power-performance relationships such as FPS-power curves, energy per frame, and performance per watt. This method allows us to approximate the performance envelope of a diverse class of GPUs, from embedded and mobile-class devices to high-end consumer-grade systems. Our objective is to explore the practical lower bounds of client-side 3DGS rasterisation and assess its potential for deployment in energy-constrained environments, including standalone headsets and thin clients. Through this analysis, we provide early insights into the performance-energy trade-offs that govern the viability of edge-deployed 3DGS systems.

翻译：我们研究了在边缘客户端上，针对不同高斯泼溅数量和GPU计算预算下，实时三维高斯泼溅（3DGS）栅格化的可行性。不同于评估多种物理设备，我们采用基于仿真的方法，在单个高端GPU上近似不同GPU能力层级。通过系统性地降低GPU核心频率并施加功率上限，我们模拟了一个受控的浮点性能范围，以近似不同GPU能力层级。在该范围的每个点上，我们测量了不同复杂度场景、流水线和优化下的帧率、运行时行为及功耗，从而分析性能-功率关系，例如FPS-功率曲线、每帧能量和每瓦性能。该方法使我们能够近似从嵌入式及移动设备到高端消费级系统等各类GPU的性能包络。我们的目标是探索客户端侧3DGS栅格化的实际下限，并评估其在能量受限环境（包括独立头戴设备和瘦客户端）中的部署潜力。通过这一分析，我们初步揭示了决定边缘部署3DGS系统可行性的性能-能量权衡关系。