3D高斯加速光线追踪：基于粒子反向传播的快速训练

中文摘要 AI 辅助

3D高斯溅射（3D Gaussian Splatting）已使高斯基元成为实时新视图合成的高效表示，但其基于光栅化的公式依赖于屏幕空间近似，限制了视图相关排序的准确性，以及反射、折射、阴影等次级光线效果的集成。高斯光线追踪（Gaussian ray tracing）通过评估显式光线-基元相交解决了这些限制，但训练成本仍很高。我们观察到主要瓶颈不仅是光线遍历，还有以像素为中心的反向传播：许多线程同时将梯度累积到同一基元参数中，导致严重的原子竞争和线程序列化。我们提出3DGART，一种用于光线追踪高斯渲染的实用训练框架。核心思路是围绕基元而非像素重组反向传播，利用保守的透视校正屏幕空间边界，构建紧凑的中间缓冲区和瓦片-基元映射，使每个线程能累积一个基元在其覆盖的瓦片内像素上的贡献，将梯度计算从竞争激烈的分散操作转换为结构化的类似收集过程。在Mip-NeRF 360数据集上，3DGART实现了比基于像素的基线快约3-3.5倍的原始训练速度，比3DGRT快约4倍，同时提升了质量。更重要的是，3DGART使全光线追踪高斯训练变得实用，达到了与基于光栅化的流水线相当的运行时间，同时保留了光线追踪的优势。

英文摘要

3D Gaussian Splatting has made Gaussian primitives a highly efficient representation for real-time novel view synthesis, but its rasterisation-based formulation relies on screen-space approximations that limit accurate view-dependent ordering and the integration of secondary ray effects such as reflections, refractions, and shadows. Gaussian ray tracing addresses these limitations by evaluating explicit ray-primitive intersections, yet it remains costly to train. We observe that the main bottleneck is not ray traversal alone, but the pixel-centric backward propagation, where many threads concurrently accumulate gradients into the same primitive parameters, causing severe atomic contention and thread serialisation. We present 3DGART, a practical training framework for ray-traced Gaussian rendering. Our key idea is to reorganise backward propagation around primitives rather than pixels. Using conservative perspective-correct screen-space bounds, we build a compact intermediate buffer and a tile-primitive mapping that allows each thread to accumulate the contribution of one primitive over its covered pixels within a tile. This transforms gradient computation from a contention-heavy scatter operation into a structured gather-like process. On Mip-NeRF 360, 3DGART achieves an $\approx 3-3.5\times$ raw training speedup over per-pixel baseline and $\approx4 \times$ over 3DGRT on Mip-NeRF 360 while improving quality. More importantly, 3DGART makes fully ray-traced Gaussian training practical, reaching runtimes competitive with rasterisation-based pipelines while preserving benefits of ray tracing.

发表机构

• University of Canterbury（坎特伯雷大学）

机构由 AI 辅助整理，请以论文原文为准。