Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussian Splatting
Jiarui Meng Haijie Li Yanmin Wu Qiankun Gao Shuzhou Yang Jian Zhang Siwei Ma
3D Gaussian Splatting (3DGS) has marked a significant breakthrough in the
realm of 3D scene reconstruction and novel view synthesis. However, 3DGS, much
like its predecessor Neural Radiance Fields (NeRF), struggles to accurately
model physical reflections, particularly in mirrors that are ubiquitous in
real-world scenes. This oversight mistakenly perceives reflections as separate
entities that physically exist, resulting in inaccurate reconstructions and
inconsistent reflective properties across varied viewpoints. To address this
pivotal challenge, we introduce Mirror-3DGS, an innovative rendering framework
devised to master the intricacies of mirror geometries and reflections, paving
the way for the generation of realistically depicted mirror reflections. By
ingeniously incorporating mirror attributes into the 3DGS and leveraging the
principle of plane mirror imaging, Mirror-3DGS crafts a mirrored viewpoint to
observe from behind the mirror, enriching the realism of scene renderings.
Extensive assessments, spanning both synthetic and real-world scenes, showcase
our method's ability to render novel views with enhanced fidelity in real-time,
surpassing the state-of-the-art Mirror-NeRF specifically within the challenging
mirror regions. Our code will be made publicly available for reproducible
research.