MR-Compare: A Mixed-Reality Framework for Spatially Grounded Visual Comparison of 3D Gaussian Splatting and Mesh Reconstructions with the Physical Environment

2026-07-22

AI Summary

This work addresses the lack of effective methods for spatially aligning and visually comparing 3D Gaussian Splatting (3DGS) reconstructions with mesh-based results in real-world environments. We propose MR-Compare, the first cross-modal 3D reconstruction comparison framework supporting spatial anchoring, built upon the Meta Quest 3’s video passthrough mixed reality platform. Our approach integrates a two-stage registration pipeline enhanced by a zero-shot anisotropic filter to improve the robustness of 3DGS alignment and introduces an interactive 3D slider for real-time visual comparison. Experiments demonstrate centimeter-level registration accuracy in real indoor scenes, validating the superiority of the 3DGS-MCMC workflow, while user studies confirm the system’s high usability and low cognitive load.

Abstract

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.

Problem

• mixed reality
• 3D Gaussian splatting
• mesh reconstruction
• spatial registration
• visual comparison

Innovation

• Mixed Reality
• 3D Gaussian Splatting
• Spatial Registration
• Anisotropy Filter
• Video See-Through