NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks (2208.04448v2)

Abstract: We introduce NeuralVDB, which improves on an existing industry standard for efficient storage of sparse volumetric data, denoted VDB [Museth 2013], by leveraging recent advancements in machine learning. Our novel hybrid data structure can reduce the memory footprints of VDB volumes by orders of magnitude, while maintaining its flexibility and only incurring small (user-controlled) compression errors. Specifically, NeuralVDB replaces the lower nodes of a shallow and wide VDB tree structure with multiple hierarchical neural networks that separately encode topology and value information by means of neural classifiers and regressors respectively. This approach is proven to maximize the compression ratio while maintaining the spatial adaptivity offered by the higher-level VDB data structure. For sparse signed distance fields and density volumes, we have observed compression ratios on the order of 10x to more than 100x from already compressed VDB inputs, with little to no visual artifacts. Furthermore, NeuralVDB is shown to offer more effective compression performance compared to other neural representations such as Neural Geometric Level of Detail [Takikawa et al. 2021], Variable Bitrate Neural Fields [Takikawa et al. 2022a], and Instant Neural Graphics Primitives [M\"uller et al. 2022]. Finally, we demonstrate how warm-starting from previous frames can accelerate training, i.e., compression, of animated volumes as well as improve temporal coherency of model inference, i.e., decompression.

• The paper presents NeuralVDB, which replaces lower VDB nodes with hierarchical neural networks to achieve 10x–100x compression with minimal visual loss.
• It leverages classifiers and regressors to encode topology and value information, significantly enhancing memory efficiency for volumetric data.
• NeuralVDB seamlessly integrates with existing VDB pipelines and boosts training speed with temporal encoding for dynamic volumetric scenes.

An Evaluation of NeuralVDB: A Neural Network Approach to Sparse Volume Representation

The paper "NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks" explores a novel approach to reducing the memory footprints of volumetric data through the application of neural networks. Building on the foundational framework of VDB, an established industry standard for handling sparse volumetric data, NeuralVDB integrates machine learning techniques to enhance data compression while maintaining effective representation.

The crux of NeuralVDB’s methodology lies in substituting the lower nodes in a VDB tree with hierarchical neural networks that encode both topology and value information through classifiers and regressors. This combination facilitates the substantial compression of already compact VDB volumes, achieving compression ratios between 10x and 100x with minimal visual loss. Such performance surpasses other similar neural representation techniques, such as Neural Geometric Level of Detail, Variable Bitrate Neural Fields, and Instant Neural Graphics Primitives.

NeuralVDB exhibits notable contributions to memory efficiency, supporting effective representation of volumetric data in storage and runtime scenarios. Through leveraging neural networks, NeuralVDB provides both out-of-core and in-memory compression, favoring scalability and adaptability over static configurations. This flexibility is particularly advantageous for exchanging volumetric data over networks or storing it in cloud environments where bandwidth and storage are at a premium.

One of the significant advancements NeuralVDB offers is its compatibility with existing VDB pipelines. This compatibility ensures a seamless transition between traditional and neural-augmented systems, facilitating its adoption without significant changes to current workflows. In addition, NeuralVDB’s temporal encoding feature, which uses neural networks to warm-start training from the previous frame, improves both the training speed and the temporal consistency for dynamic volumetric data, demonstrating its utility in fields like computer graphics and simulation of time-series data.

The paper provides convincing experimental evidence that demonstrates high compression ratios without sacrificing data fidelity. For example, by reducing the Disney Cloud dataset's size from 1.5 GB to 25 MB, NeuralVDB illustrates its potential in a practical context. This aspect is emphasized further when discussing animated datasets, highlighting acceleration in training and enhanced temporal coherence of reconstructions.

Despite its improvements, NeuralVDB is not without limitations. Its reliance on neural network training and inference can introduce reconstruction artifacts, particularly in data sets with high frequency details, such as very thin geometric features or sudden transitions in density data. Nevertheless, these challenges can potentially be addressed with improvements in hyperparameter optimization or more advanced network architectures. Additionally, while NeuralVDB presently assumes a fixed tree structure, future advancements could explore dynamic adaptations, boosting its applicability further in evolving data scenarios.

As AI continues to grow and impact volumetric data management, NeuralVDB posits a meaningful shift towards more resource-efficient storage and visualization. Its implications span multiple domains, including medical imaging, computer-aided design, and scientific computing, where balancing memory usage and data fidelity remains a constant challenge. The research points towards a future where neural networks play a central role in volumetric data representation, ushering in improvements in both rendering processes and data compression techniques. In conclusion, while NeuralVDB shows promising advancements, there remain opportunities for further refinement and exploration, particularly in its adaptability to dynamic and complex volumetric spaces.