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MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections

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arxiv 2405.11921 v1 pith:26SIYRGQ submitted 2024-05-20 cs.CV

classification cs.CV
keywords mirrorgaussiansmirrorgaussianreal-timegaussianmirrorsplanereal-world
verification ladder T0 review T1 audit T2 compute T3 formal
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3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting. The key insight is grounded on the mirror symmetry between the real-world space and the virtual mirror space. We introduce an intuitive dual-rendering strategy that enables differentiable rasterization of both the real-world 3D Gaussians and the mirrored counterpart obtained by reflecting the former about the mirror plane. All 3D Gaussians are jointly optimized with the mirror plane in an end-to-end framework. MirrorGaussian achieves high-quality and real-time rendering in scenes with mirrors, empowering scene editing like adding new mirrors and objects. Comprehensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods, achieving state-of-the-art results. Project page: https://mirror-gaussian.github.io/.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. NeRFs are Mirror Detectors: Using Structural Similarity for Multi-View Mirror Scene Reconstruction with 3D Surface Primitives

    cs.CV 2025-01 conditional novelty 6.0 of 10

    NeRF-MD automatically detects mirrors from the photometric inconsistencies left by a standard NeRF and reconstructs scenes with explicit mirror primitives, without user-provided masks.

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