A Query-Efficient Stochastic Volume Rendering Framework for Time-Varying Implicit Neural Volumes
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-e...
| Authors | Alper Sahistan, Haichao Miao, Zhimin Li et al. |
|---|---|
| Primary category | cs.GR |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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