STAR-NT: Spatiotemporal Acceleration of Real-Time Neural Transparency Rendering

Grigoris Tsopouridis; Christos Georgiou-Mousses; Aris Panagiotidis; Andreas Vasilakis; David Corrigan; Tobias A. Franke; Aleksei Gorbonosov; Andrei Astapov

cs.GRJun 15, 2026

STAR-NT: Spatiotemporal Acceleration of Real-Time Neural Transparency Rendering

Authors: Grigoris Tsopouridis, Christos Georgiou-Mousses, Aris Panagiotidis, Andreas Vasilakis, David Corrigan, Tobias A. Franke, Aleksei Gorbonosov, Andrei Astapov, +1 more

Organizations: University of Ioannina, Greece · Athens University of Economics and Business, Greece · Huawei Ireland Research Center, Ireland

Abstract

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.

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A Query-Efficient Stochastic Volume Rendering Framework for Time-Varying Implicit Neural Volumes

Alper Sahistan, Haichao Miao, Zhimin Li, Peer-Timo Bremer+2

Time-varying implicit neural representations (INRs) provide a compact representation of scientific volumes and, for modalities such as dynamic X-ray computed tomography (CT), are often the only practical way to represent the data. However, interactive volume rendering of INRs is challenging, as cheap memory lookups are replaced by expensive neural inferences, hindering the performance. Therefore, conventional volume rendering methods such as ray marching with dense sampling are often impractical. While resampling, caching, and retraining can mitigate this cost, they compromise convenience and accuracy and become impractical for time-varying data. We tackle these challenges using a query-efficient stochastic volume rendering framework based on delta tracking. Our system employs a four-stage pipeline that exploits heterogeneous parallelism, using ray tracing cores for traversal and tensor cores for batched neural evaluation. Furthermore, we present strategies to reduce INR queries via ray budgeting and query pruning, thereby increasing per-frame performance. Using our renderer, many time-varying INRs can be rendered directly from their original representation. The system achieves ~30-40 FPS at 1024x1024 resolution on an RTX 4090 GPU and converges to high-fidelity images. Moreover, the system enables interactive temporal exploration of the continuous domain, with timestep updates taking approximately 1-2 ms.

Neural Rendering Volume Implicit Neural Representations Tomography Vr Transmissive X-Ray Imaging

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Abstract

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