STAR-NT: Spatiotemporal Acceleration of Real-Time Neural Transparency Rendering
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 integr...
| Authors | Grigoris Tsopouridis, Christos Georgiou-Mousses, Aris Panagiotidis et al. |
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| Primary category | cs.GR |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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