用统计矩提升3D高斯点云的透明度渲染，无需排序或光线追踪。
Moment-Based 3D Gaussian Splatting: Resolving Volumetric Occlusion with Order-Independent Transmittance
- 基于像素级统计矩表征光线密度分布，实现无序透明计算。
- 在复杂重叠半透明物体上显著提升重建与渲染质量。
- 适合追求真实感、高精度透明效果的3D重建与渲染应用。
3D高斯点云（3DGS）通过快速优化与实时渲染重塑了新视角合成技术，但其依赖简化的顺序相关透明度混合和密度积分粗略近似，难以准确渲染复杂重叠的半透明物体。本文提出一种新颖的体素透明度计算方法，完全避免光线追踪和逐像素采样排序。基于前人关于基于矩的无序透明性工作，核心思路是利用统计矩对每条相机射线上的密度分布进行紧凑连续建模。我们从所有贡献的3D高斯中解析推导并计算一组像素级矩。由此重构出每条射线的连续透明度函数，并独立在每个高斯内采样。该方法在光路衰减建模上实现了接近物理真实的渲染效果，显著提升了复杂半透明介质的重建与渲染质量。
原文摘要 · Abstract (English)
The recent success of 3D Gaussian Splatting (3DGS) has reshaped novel view synthesis by enabling fast optimization and real-time rendering of high-quality radiance fields. However, it relies on simplified, order-dependent alpha blending and coarse approximations of the density integral within the rasterizer, thereby limiting its ability to render complex, overlapping semi-transparent objects. In this paper, we extend rasterization-based rendering of 3D Gaussian representations with a novel method for high-fidelity transmittance computation, entirely avoiding the need for ray tracing or per-pixel sample sorting. Building on prior work in moment-based order-independent transparency, our key idea is to characterize the density distribution along each camera ray with a compact and continuous representation based on statistical moments. To this end, we analytically derive and compute a set of per-pixel moments from all contributing 3D Gaussians. From these moments, a continuous transmittance function is reconstructed for each ray, which is then independently sampled within each Gaussian. As a result, our method bridges the gap between rasterization and physical accuracy by modeling light attenuation in complex translucent media, significantly improving overall reconstruction and rendering quality.
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