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

• URL: http://arxiv.org/abs/2607.28047v1
• Date: Thu, 30 Jul 2026 11:27:06 GMT
• Title: A Query-Efficient Stochastic Volume Rendering Framework for Time-Varying Implicit Neural Volumes
• Abstract summary: Time-varying implicit neural representations (INRs) provide a compact representation of scientific volumes.<n>INRs are often the only practical way to represent the data.<n>We present a query-efficient volume rendering framework based on delta tracking.
• Score: 18.14414428131909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields [6.66530903309279]
  We propose a spike-based NeRF framework with a dynamic time step training strategy, termed Pretrain-Adaptive Time-step Adjustment (PATA)<n>We show that PATA can preserve rendering fidelity while reducing inference time steps by 64% and running power by 61.55%.
  arXiv  Detail & Related papers  (2025-07-30T18:56:24Z)
• N-BVH: Neural ray queries with bounding volume hierarchies [51.430495562430565]
  In 3D computer graphics, the bulk of a scene's memory usage is due to polygons and textures. We devise N-BVH, a neural compression architecture designed to answer arbitrary ray queries in 3D. Our method provides faithful approximations of visibility, depth, and appearance attributes.
  arXiv  Detail & Related papers  (2024-05-25T13:54:34Z)
• RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing [1.534667887016089]
  MonteCarlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts. We propose a framework with end-to-end training of a sampling importance network, a latent space encoder network, and a denoiser network.
  arXiv  Detail & Related papers  (2023-10-05T12:39:27Z)
• HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation [31.44962361819199]
  Implicit Neural Representations (INRs) have recently exhibited immense potential in the field of scientific visualization. In this paper, we introduce HyperINR, a novel hypernetwork architecture capable of directly predicting the weights for a compact INR. By harnessing an ensemble of multiresolution hash encoding units in unison, the resulting INR attains state-of-the-art inference performance.
  arXiv  Detail & Related papers  (2023-04-09T08:10:10Z)
• Re-ReND: Real-time Rendering of NeRFs across Devices [56.081995086924216]
  Re-ReND is designed to achieve real-time performance by converting the NeRF into a representation that can be efficiently processed by standard graphics pipelines. We find that Re-ReND can achieve over a 2.6-fold increase in rendering speed versus the state-of-the-art without perceptible losses in quality.
  arXiv  Detail & Related papers  (2023-03-15T15:59:41Z)
• HyperTime: Implicit Neural Representation for Time Series [131.57172578210256]
  Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset.
  arXiv  Detail & Related papers  (2022-08-11T14:05:51Z)
• Interactive Volume Visualization via Multi-Resolution Hash Encoding based Neural Representation [29.797933404619606]
  We show that we can interactively ray trace volumetric neural representations (10-60fps) using modern GPU cores and a well-designed rendering algorithm. Our neural representations are also high-fidelity teracell (PSNR > 30dB) and compact (10-1000x smaller) To support extreme-scale volume data, we also develop an efficient out-of-core training strategy, which allows our neural representation training to potentially scale up to terascale.
  arXiv  Detail & Related papers  (2022-07-23T23:04:19Z)
• Differentiable Point-Based Radiance Fields for Efficient View Synthesis [57.56579501055479]
  We propose a differentiable rendering algorithm for efficient novel view synthesis. Our method is up to 300x faster than NeRF in both training and inference. For dynamic scenes, our method trains two orders of magnitude faster than STNeRF and renders at near interactive rate.
  arXiv  Detail & Related papers  (2022-05-28T04:36:13Z)
• InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering [55.70938412352287]
  We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural implicit representation. The proposed approach minimizes potential reconstruction inconsistency that happens due to insufficient viewpoints. We achieve consistently improved performance compared to existing neural view synthesis methods by large margins on multiple standard benchmarks.
  arXiv  Detail & Related papers  (2021-12-31T11:56:01Z)
• Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations [10.484851004093919]
  Implicit neural representations (INRs) are neural networks used to approximate low-dimensional functions. In this work we shed light on the potential of Conditional Implicit Neural Representations (CINRs) as lightweight backbones in generative frameworks for audio synthesis.
  arXiv  Detail & Related papers  (2021-11-14T13:36:18Z)
• Real-time Neural Radiance Caching for Path Tracing [67.46991813306708]
  We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials. We demonstrate significant noise reduction at the cost of little induced bias, and report state-of-the-art, real-time performance on a number of challenging scenarios.
  arXiv  Detail & Related papers  (2021-06-23T13:09:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.