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MR-Compare: A Mixed-Reality Framework for Spatially Grounded Visual Comparison of 3D Gaussian Splatting and Mesh Reconstructions with the Physical Environment
Changrui Zhu, Ernst Kruijff, Pengju Zhang, Simon Julier
https://arxiv.org/abs/2607.20325 https://arxiv.org/pdf/2607.20325 https://arxiv.org/html/2607.20325
arXiv:2607.20325v1 Announce Type: new
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.
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