用高斯点云降低机器人混合现实通信能耗，10倍节能。
Green Robotic Mixed Reality with Gaussian Splatting
- 用高斯点云建模环境，按需渲染画面减少上传
- 实测通信能耗降低超10倍，画质指标更优
- 适合关注绿色通信与低功耗视觉系统的研究者
在机器人混合现实（RoboMR）系统中实现绿色通信面临挑战，因需通过无线信道高频上传高分辨率图像。本文提出高斯点云机器人混合现实（GSRMR），显著降低能耗，推动绿色RoboMR发展。核心在于构建高斯点云（GS）模型，使仿真器能根据机器人位姿选择性生成逼真视角，减少冗余图像上传。由于GS模型与真实环境存在偏差，进一步提出跨层优化框架（GSCLO），联合优化内容切换（决定是否上传图像）与各帧功率分配。通过加速惩罚优化（APO）算法求解该问题。实验表明，所提GSRMR相比传统RoboMR通信能耗降低超过10倍；且结合APO的方案在峰值信噪比（PSNR）和结构相似性指数（SSIM）上优于多种基线方法。
原文摘要 · Abstract (English)
Realizing green communication in robotic mixed reality (RoboMR) systems presents a challenge, due to the necessity of uploading high-resolution images at high frequencies through wireless channels. This paper proposes Gaussian splatting (GS) RoboMR (GSRMR), which achieves a lower energy consumption and makes a concrete step towards green RoboMR. The crux to GSRMR is to build a GS model which enables the simulator to opportunistically render a photo-realistic view from the robot's pose, thereby reducing the need for excessive image uploads. Since the GS model may involve discrepancies compared to the actual environments, a GS cross-layer optimization (GSCLO) framework is further proposed, which jointly optimizes content switching (i.e., deciding whether to upload image or not) and power allocation across different frames. The GSCLO problem is solved by an accelerated penalty optimization (APO) algorithm. Experiments demonstrate that the proposed GSRMR reduces the communication energy by over 10x compared with RoboMR. Furthermore, the proposed GSRMR with APO outperforms extensive baseline schemes, in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
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