Neural order-independent transparency delivers high-quality rendering of overlapping transparent surfaces, but its geometry passes and network input generation remain costly, particularly on mobile and legacy hardware. We present a spatiotemporal acceleration framework that exploits spatial and temporal coherence to reduce this overhead while preserving visual quality. Spatially, we use adaptive quadtree-based screen-space subdivision to scale geometry pass resolution according to local color variance. Temporally, selected frames reuse the previous transparency result through depth-based reprojection instead of full rendering. Together, these optimizations reduce rendering cost and integrate efficiently into existing real-time rendering pipelines.

翻译：神经顺序无关透明度能够高质量渲染重叠透明表面，但其几何处理阶段与网络输入生成仍存在较高计算成本，尤其在移动端和传统硬件上尤为显著。我们提出一种利用空间和时间连贯性降低开销的时空加速框架，同时保持视觉质量。在空间维度上，我们采用基于自适应四叉树的屏幕空间细分方法，根据局部颜色方差调整几何处理分辨率。时间维度上，通过深度重投影复用选定帧的先前透明度结果，替代完整渲染。这些优化组合有效降低了渲染成本，并能高效集成到现有实时渲染管线中。