Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparency
<|reference_start|>Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparency: 3D Gaussian Splats (3DGS) have proven a versatile rendering primitive, both for inverse rendering as well as real-time exploration of scenes. In these applications, coherence across camera frames and multiple views is crucial, be it for robust convergence of a scene reconstruction or for artifact-free fly-throughs. Recent work started mitigating artifacts that break multi-view coherence, including popping artifacts due to inconsistent transparency sorting and perspective-correct outlines of (2D) splats. At the same time, real-time requirements forced such implementations to accept compromises in how transparency of large assemblies of 3D Gaussians is resolved, in turn breaking coherence in other ways. In our work, we aim at achieving maximum coherence, by rendering fully perspective-correct 3D Gaussians while using a high-quality approximation of accurate blending, hybrid transparency, on a per-pixel level, in order to retain real-time frame rates. Our fast and perspectively accurate approach for evaluation of 3D Gaussians does not require matrix inversions, thereby ensuring numerical stability and eliminating the need for special handling of degenerate splats, and the hybrid transparency formulation for blending maintains similar quality as fully resolved per-pixel transparencies at a fraction of the rendering costs. We further show that each of these two components can be independently integrated into Gaussian splatting systems. In combination, they achieve up to 2$\times$ higher frame rates, 2$\times$ faster optimization, and equal or better image quality with fewer rendering artifacts compared to traditional 3DGS on common benchmarks.<|reference_end|>
@article{hahlbohm2024efficient,
    title={Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid
  Transparency},
    author={Florian Hahlbohm, Fabian Friederichs, Tim Weyrich, Linus Franke,
  Moritz Kappel, Susana Castillo, Marc Stamminger, Martin Eisemann, Marcus
  Magnor},
    journal={arXiv preprint arXiv:2410.08129},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.08129},
    primaryClass={cs.GR cs.CV}
}