改进3D高斯点云渲染中的透明度融合,解决缩放时的模糊和阶梯伪影。
Gaussian Blending: Rethinking Alpha Blending in 3D Gaussian Splatting
- 用空间分布的透明度替代传统标量融合,动态调整背景贡献
- 在未见采样率下仍能保持细节,显著减少缩放时的视觉失真
- 无需额外计算成本,可直接替换现有3DGS系统
3D高斯点云(3DGS)的引入极大推动了新视角合成技术的发展。尽管已有研究提升了渲染质量,但在训练中未见的采样率下仍存在明显视觉偏差:放大时出现侵蚀性模糊,缩小时产生扩张性阶梯伪影。我们推测这些现象源于3DGS中采用的标量透明度融合机制的根本局限。为此,提出新型高斯融合(Gaussian Blending),将透明度与透射率视为像素区域内的空间分布函数,从而根据透明度分布动态更新透射率,使邻近背景点云可参与最终渲染。该方法保持实时渲染速度,不增加内存开销,且可作为即插即用模块集成至现有3DGS或其它新视角合成框架中。大量实验表明,该方法在各种未见采样率下均能有效保留细粒度结构,持续优于现有新视角合成模型,无论在已见还是未见采样率下表现更佳。
原文摘要 · Abstract (English)
The recent introduction of 3D Gaussian Splatting (3DGS) has significantly advanced novel view synthesis. Several studies have further improved the rendering quality of 3DGS, yet they still exhibit noticeable visual discrepancies when synthesizing views at sampling rates unseen during training. Specifically, they suffer from (i) erosion-induced blurring artifacts when zooming in and (ii) dilation-induced staircase artifacts when zooming out. We speculate that these artifacts arise from the fundamental limitation of the alpha blending adopted in 3DGS methods. Instead of the conventional alpha blending that computes alpha and transmittance as scalar quantities over a pixel, we propose to replace it with our novel Gaussian Blending that treats alpha and transmittance as spatially varying distributions. Thus, transmittances can be updated considering the spatial distribution of alpha values across the pixel area, allowing nearby background splats to contribute to the final rendering. Our Gaussian Blending maintains real-time rendering speed and requires no additional memory cost, while being easily integrated as a drop-in replacement into existing 3DGS-based or other NVS frameworks. Extensive experiments demonstrate that Gaussian Blending effectively captures fine details at various sampling rates unseen during training, consistently outperforming existing novel view synthesis models across both unseen and seen sampling rates.
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