MR-Compare：用于将3D高斯点渲染和网格重建与物理环境进行空间视觉比较的混合现实框架
MR-Compare: A Mixed-Reality Framework for Spatially Grounded Visual Comparison of 3D Gaussian Splatting and Mesh Reconstructions with the Physical Environment
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中文总结 AI 辅助
介绍MR-Compare混合现实框架，用于3D高斯点渲染和网格重建与物理环境的空间视觉比较。结合两阶段配准管道与3D滑块，评估多种重建工作流程，实现厘米级误差，提出各向异性滤波器，建立了系统级可行性。
中文摘要 AI 辅助
我们介绍了MR-Compare，这是一个用于将3D高斯点渲染和网格重建通过实时视频透视（VST）进行空间视觉比较的混合现实框架。它在连接到PC的Meta Quest 3上实现，将两阶段配准管道与用于跨媒体比较的3D滑块相结合。我们通过在两个静态室内房间进行的真实世界基准测试和探索性用户研究（n = 30）评估了五种代表性的桌面和移动重建工作流程。MR-Compare在所有工作流程中实现了厘米级的平移误差。两个桌面3DGS工作流程显示出最强的整体模式，3DGS-MCMC产生了最低的配准误差和最强的VST参考视觉一致性。房间会话测量表明感知可用性高且工作量低。我们进一步提出了一种各向异性滤波器，这是一个利用高斯各向异性来改进MR-Compare中3DGS配准的零样本模块。受控的副本阈值扫描表明适度修剪可以提高鲁棒性并减少残余误差。这些结果在测试设置中建立了系统级可行性，而非任务级有效性或独立部署可行性。该项目可通过此https URL获取。
英文摘要
We introduce MR-Compare, a mixed reality framework for spatially grounded visual comparison between 3D Gaussian splatting and mesh reconstructions with live video see-through (VST). Implemented on a PC-tethered Meta Quest~3, it combines a two-stage registration pipeline with a 3D Slider for cross-media comparison. We evaluated five representative desktop and mobile reconstruction workflows through a real-world benchmark with an exploratory user study ($n=30$) in two static indoor rooms. MR-Compare achieved centimetre-level translation error across all workflows. The two desktop 3DGS workflows showed the strongest overall pattern, with 3DGS-MCMC yielding the lowest registration error and strongest VST-referenced visual consistency. Room-session measures indicated high perceived usability and low workload. We further propose an anisotropy filter, a zero-shot module that leverages Gaussian anisotropies to improve 3DGS registration in MR-Compare. A controlled Replica threshold sweep shows that moderate pruning can improve robustness and reduce residual errors. These results establish system-level feasibility in the tested setting rather than task-level effectiveness or standalone deployment. The project is available at https://github.com/changruizhu96/MR-Compare.
